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lecturebank.tsv
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1 NLP Resources http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/133.pptx 1065 2018 Dragomir Radev nlp Yale
2 Syntax-based Machine Translation http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/455.pptx 1147 2018 Dragomir Radev nlp Yale
3 Generative and Discriminative Models 1 http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/513.pptx 1155 2018 Dragomir Radev nlp Yale
4 Tools for Deep Learning http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/731.pptx 1189 2018 Dragomir Radev nlp Yale
5 Python Basics http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/131.pptx 1063 2018 Dragomir Radev nlp Yale
6 Social Network Extraction http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/976.pptx 1546 2018 Dragomir Radev nlp Yale
7 Inference http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/364.pptx 1217 2018 Dragomir Radev nlp Yale
8 Clustering 1 http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/571.pptx 1167 2018 Dragomir Radev nlp Yale
9 Prepositional Phrase Attachment 2 http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/283.pptx 1536 2018 Dragomir Radev nlp Yale
10 Long Short Term Memory Networks http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/742.pptx 1191 2018 Dragomir Radev nlp Yale
11 Class Logistics http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/111.pptx 1580 2018 Dragomir Radev nlp Yale
12 Smoothing and Interpolation http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/212.pptx 1082 2018 Dragomir Radev nlp Yale
13 Wordnet http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/315.pptx 1112 2018 Dragomir Radev nlp Yale
14 Question Answering Basics http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/411.pptx 1125 2018 Dragomir Radev nlp Yale
15 Examples of Text Classification http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/542.pptx 1162 2018 Dragomir Radev nlp Yale
16 Features and Unification http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/262.pptx 1102 2018 Dragomir Radev nlp Yale
17 Morphology and Semantics in Machine Translation http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/457.pptx 1149 2018 Dragomir Radev nlp Yale
18 Sentence Simplification http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/426.pptx 1134 2018 Dragomir Radev nlp Yale
19 Introduction to Hidden Markov Models http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/221.pptx 1086 2018 Dragomir Radev nlp Yale
20 Semi Supervised Retrieval, Pagerank http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/624.pptx 1176 2018 Dragomir Radev ir Yale
21 NLP Tasks 2 http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/152.pptx 1072 2018 Dragomir Radev nlp Yale
22 Preprocessing http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/153.pptx 1073 2018 Dragomir Radev nlp Yale
23 Machine Translation Techniques http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/452.pptx 1144 2018 Dragomir Radev nlp Yale
24 Discourse Parsing http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/443.pptx 1140 2018 Dragomir Radev nlp Yale
25 Regular Expressions http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/161.pptx 1074 2018 Dragomir Radev nlp Yale
26 Why is NLP Hard? http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/114.pptx 1054 2018 Dragomir Radev nlp Yale
27 Classic Parsing Methods http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/243.pptx 1094 2018 Dragomir Radev nlp Yale
28 Neural Question Answering http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/755.pptx 1199 2018 Dragomir Radev nlp Yale
29 QA Architecture and QA Systems 1 http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/413.pptx 1127 2018 Dragomir Radev nlp Yale
30 Noisy Channel Methods for Machine Translation http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/453.pptx 1145 2018 Dragomir Radev nlp Yale
31 Linguistics Basics http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/141.pptx 1067 2018 Dragomir Radev nlp Yale
32 Introduction to Support Vector Machines and Kernels http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/521.pptx 1161 2018 Dragomir Radev nlp Yale
33 Semantic Similarity http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/314.pptx 1111 2018 Dragomir Radev nlp Yale
34 Text Kernels http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/342.pptx 1581 2018 Dragomir Radev nlp Yale
35 Finite State Transducers http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/163.pptx 1076 2018 Dragomir Radev nlp Yale
36 Parsing and Context-Free Grammars http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/242.pptx 1093 2018 Dragomir Radev nlp Yale
37 Natural Language Generation Systems http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/432.pptx 1136 2018 Dragomir Radev nlp Yale
38 NLP Tasks 1 http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/151.pptx 1071 2018 Dragomir Radev nlp Yale
39 SyntaxNet http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/911.pptx 1206 2018 Dragomir Radev nlp Yale
40 Coherence and Prosody http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/442.pptx 1139 2018 Dragomir Radev nlp Yale
41 Word Distributions http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/144.pptx 1070 2018 Dragomir Radev nlp Yale
42 Search Engine Architecture http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/612.pptx 1170 2018 Dragomir Radev ir Yale
43 Parsing Evaluation http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/247.pptx 1562 2018 Dragomir Radev nlp Yale
44 Recursive Neural Networks http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/743.pptx 1192 2018 Dragomir Radev nlp Yale
45 Semantic Role Labeling http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/367.pptx 1220 2018 Dragomir Radev nlp Yale
46 Methods used in NLP http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/115.pptx 1055 2018 Dragomir Radev nlp Yale
47 Evaluation of Language Modeling http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/213.pptx 1083 2018 Dragomir Radev nlp Yale
48 Phrase Based Machine Translation, Evaluation http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/456.pptx 1148 2018 Dragomir Radev nlp Yale
49 Summarization Evaluation http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/425.pptx 1133 2018 Dragomir Radev nlp Yale
50 Introduction to Word Sense Disambiguation http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/391.pptx 1124 2018 Dragomir Radev nlp Yale
51 Practical Considerations of Classification http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/545.pptx 1165 2018 Dragomir Radev nlp Yale
52 Sentiment Analysis http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/381.pptx 1122 2018 Dragomir Radev nlp Yale
53 Bayes Theorem http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/125.pptx 1060 2018 Dragomir Radev nlp Yale
54 Query Modification http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/622.pptx 1174 2018 Dragomir Radev ir Yale
55 Unlexicalized Parsing http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/254.pptx 1100 2018 Dragomir Radev nlp Yale
56 Evaluation of Question Answering http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/412.pptx 1126 2018 Dragomir Radev nlp Yale
57 QA Architecture and QA Systems 2 http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/414.pptx 1128 2018 Dragomir Radev nlp Yale
58 Collocations http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/352.pptx 1118 2018 Dragomir Radev nlp Yale
59 Morphology and Lexicon http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/143.pptx 1069 2018 Dragomir Radev nlp Yale
60 Introduction to Information Retrieval http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/611.pptx 1169 2018 Dragomir Radev ir Yale
61 Introduction to Language Modeling http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/211.pptx 1081 2018 Dragomir Radev nlp Yale
62 Probabilities 2 http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/124.pptx 1059 2018 Dragomir Radev nlp Yale
63 Summarization 4 http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/424.pptx 1132 2018 Dragomir Radev nlp Yale
64 Lexicalized Parsing http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/253.pptx 1280 2018 Dragomir Radev nlp Yale
65 Edit Distance http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/313.pptx 1110 2018 Dragomir Radev nlp Yale
66 Neural Architectures, Training Neural Networks http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/712.pptx 1182 2018 Dragomir Radev nlp Yale
67 Prepositional Phrase Attachment 3 http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/284.pptx 1537 2018 Dragomir Radev nlp Yale
68 The Noisy Channel http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/214.pptx 1084 2018 Dragomir Radev nlp Yale
69 Natural Language Generation Basics http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/431.pptx 1135 2018 Dragomir Radev nlp Yale
70 Introduction to Dimensionality Reduction http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/341.pptx 1116 2018 Dragomir Radev nlp Yale
71 Summarization 1 http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/421.pptx 1129 2018 Dragomir Radev nlp Yale
72 Language and Complexity http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/166.pptx 1079 2018 Dragomir Radev nlp Yale
73 Stemming http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/312.pptx 1109 2018 Dragomir Radev nlp Yale
74 Machine Translation Basics http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/451.pptx 1143 2018 Dragomir Radev nlp Yale
75 Introduction to Part of Speech Tagging http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/215.pptx 1085 2018 Dragomir Radev nlp Yale
76 Neural Summarization http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/754.pptx 1198 2018 Dragomir Radev nlp Yale
77 Dialogue Systems http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/445.pptx 1142 2018 Dragomir Radev nlp Yale
78 Dialogue http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/444.pptx 1141 2018 Dragomir Radev nlp Yale
79 Random Walks and Harmonic Functions http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/623.pptx 1175 2018 Dragomir Radev nlp Yale
80 Entailment and Paraphrasing http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/351.pptx 1117 2018 Dragomir Radev nlp Yale
81 Sentiment Lexicons http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/382.pptx 1123 2018 Dragomir Radev nlp Yale
82 NACLO Problems on Parsing http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/271.pptx 1105 2018 Dragomir Radev nlp Yale
83 Classification and kNN 1 http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/511.pptx 1153 2018 Dragomir Radev nlp Yale
84 Toolkits for Information Retrieval http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/614.pptx 1172 2018 Dragomir Radev nlp Yale
85 Neural Machine Translation http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/753.pptx 1197 2018 Dragomir Radev nlp Yale
86 Penn Treebank http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/246.pptx 1097 2018 Dragomir Radev nlp Yale
87 Summarization 3 http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/423.pptx 1131 2018 Dragomir Radev nlp Yale
88 Thesaurus-based Similarity http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/316.pptx 1113 2018 Dragomir Radev nlp Yale
89 Evaluation of Information Retrieval http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/613.pptx 1171 2018 Dragomir Radev ir Yale
90 Alternative Syntactic Formalisms http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/261.pptx 1101 2018 Dragomir Radev nlp Yale
91 Logistic Regression 1 http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/516.pptx 1158 2018 Dragomir Radev nlp Yale
92 Discourse Analysis http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/441.pptx 1138 2018 Dragomir Radev nlp Yale
93 Information Extraction and Named Entity Recognition http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/232.pptx 1089 2018 Dragomir Radev nlp Yale
94 Miscellaneous Projects http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/921.pptx 1207 2018 Dragomir Radev nlp Yale
95 Introduction to Semantics and Logic http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/361.pptx 1120 2018 Dragomir Radev nlp Yale
96 Prepositional Phrase Attachment 1 http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/282.pptx 1107 2018 Dragomir Radev nlp Yale
97 Relation Extraction http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/233.pptx 1090 2018 Dragomir Radev nlp Yale
98 Linear Algebra 2 http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/122.pptx 1057 2018 Dragomir Radev nlp Yale
99 Convolutional Neural Networks http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/744.pptx 1193 2018 Dragomir Radev nlp Yale
100 Vector Representations http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/321.pptx 1114 2018 Dragomir Radev nlp Yale
101 Knowledge Representation http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/363.pptx 1216 2018 Dragomir Radev nlp Yale
102 Parts of Speech http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/142.pptx 1068 2018 Dragomir Radev nlp Yale
103 Neural Generation, Dialogue Systems http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/756.pptx 1200 2018 Dragomir Radev nlp Yale
104 Statistical Parsing http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/252.pptx 1099 2018 Dragomir Radev nlp Yale
105 Neural Parsing http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/752.pptx 1196 2018 Dragomir Radev nlp Yale
106 Summarization 2 http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/422.pptx 1130 2018 Dragomir Radev nlp Yale
107 Introduction to Neural Networks and Deep Learning http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/711.pptx 1181 2018 Dragomir Radev nlp Yale
108 Tree Adjoining Grammar http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/263.pptx 1103 2018 Dragomir Radev nlp Yale
109 The IBM Models http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/454.pptx 1146 2018 Dragomir Radev nlp Yale
110 Recurrent Neural Networks http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/741.pptx 1190 2018 Dragomir Radev nlp Yale
111 Learning for Hidden Markov Models http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/222.pptx 1087 2018 Dragomir Radev nlp Yale
112 Perceptron http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/515.pptx 1157 2018 Dragomir Radev nlp Yale
113 First Order Logic http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/362.pptx 1121 2018 Dragomir Radev nlp Yale
114 NN Sequence Parsing http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/281.pptx 1106 2018 Dragomir Radev nlp Yale
115 Dependency Syntax http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/255.pptx 1281 2018 Dragomir Radev nlp Yale
116 Classification and kNN 2 http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/512.pptx 1154 2018 Dragomir Radev nlp Yale
117 Evaluation of Dependency Parsing http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/258.pptx 1284 2018 Dragomir Radev nlp Yale
118 Earley Parsing http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/245.pptx 1096 2018 Dragomir Radev nlp Yale
119 Semantic Parsing http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/365.pptx 1218 2018 Dragomir Radev nlp Yale
120 Word Embeddings http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/721.pptx 1186 2018 Dragomir Radev nlp Yale
121 Transition-based Dependency Parsing http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/257.pptx 1545 2018 Dragomir Radev nlp Yale
122 NLTK http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/132.pptx 1064 2018 Dragomir Radev nlp Yale
123 Combinatory Categorial Grammar http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/264.pptx 1104 2018 Dragomir Radev nlp Yale
124 Vector Semantics http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/322.pptx 1115 2018 Dragomir Radev nlp Yale
125 History of NLP http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/113.pptx 1053 2018 Dragomir Radev nlp Yale
126 Introduction to Dependency Parsing http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/256.pptx 1282 2018 Dragomir Radev nlp Yale
127 Probabilities 1 http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/123.pptx 1058 2018 Dragomir Radev nlp Yale
128 Machine Learning Resources http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/134.pptx 1066 2018 Dragomir Radev nlp Yale
129 CKY Parsing http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/244.pptx 1095 2018 Dragomir Radev nlp Yale
130 Text Similarity Intro http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/311.pptx 1108 2018 Dragomir Radev nlp Yale
131 Introduction http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/112.pptx 1052 2018 Dragomir Radev nlp Yale
132 Feature Selection http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/543.pptx 1163 2018 Dragomir Radev nlp Yale
133 Probabilistic Grammars http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/251.pptx 1098 2018 Dragomir Radev nlp Yale
134 Natural Language Generation: Features and Unification http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/433.pptx 1137 2018 Dragomir Radev nlp Yale
135 Syntax Basics http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/241.pptx 1092 2018 Dragomir Radev nlp Yale
136 Evaluation of Text Classification http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/544.pptx 1164 2018 Dragomir Radev nlp Yale
137 Statistical Part of Speech Tagging http://www.cs.yale.edu/homes/radev/nlpclass/slides2018/231.pptx 1088 2018 Dragomir Radev nlp Yale
138 Logic I https://web.stanford.edu/class/cs221/2017/lectures/logic1.pdf 1120 2017 Percy Liang ai Stanford
139 Games II https://web.stanford.edu/class/cs221/2017/lectures/games2.pdf 1307 2017 Percy Liang ai Stanford
140 Bayesian networks III https://web.stanford.edu/class/cs221/2017/lectures/bayes3.pdf 1330 2017 Percy Liang ai Stanford
141 MDPs II https://web.stanford.edu/class/cs221/2017/lectures/mdp2.pdf 1176 2017 Percy Liang ai Stanford
142 Learning Sklearn https://web.stanford.edu/class/cs221/2017/lectures/section2.pdf 1331 2017 Percy Liang ai Stanford
143 CSPs II https://web.stanford.edu/class/cs221/2017/lectures/csp2.pdf 1309 2017 Percy Liang ai Stanford
144 AlphaGo https://web.stanford.edu/class/cs221/2017/lectures/section5.pdf 1307 2017 Percy Liang ai Stanford
145 Bayesian networks I https://web.stanford.edu/class/cs221/2017/lectures/bayes1.pdf 1330 2017 Percy Liang ai Stanford
146 Machine learning I https://web.stanford.edu/class/cs221/2017/lectures/learning1.pdf 1066 2017 Percy Liang ai Stanford
147 Constraint Satisfaction Problems (CSPs) https://web.stanford.edu/class/cs221/2017/lectures/section6.pdf 1309 2017 Percy Liang ai Stanford
148 Games I https://web.stanford.edu/class/cs221/2017/lectures/games1.pdf 1307 2017 Percy Liang ai Stanford
149 Machine learning II https://web.stanford.edu/class/cs221/2017/lectures/learning2.pdf 1066 2017 Percy Liang ai Stanford
150 Machine learning III https://web.stanford.edu/class/cs221/2017/lectures/learning3.pdf 1066 2017 Percy Liang ai Stanford
151 Introduction to Machine Learning https://web.stanford.edu/class/cs221/2017/lectures/section1.pdf 1066 2017 Percy Liang ai Stanford
152 Deep Learning https://web.stanford.edu/class/cs221/2017/lectures/deep.pdf 1181 2017 Percy Liang ai Stanford
153 Overview and review https://web.stanford.edu/class/cs221/2017/lectures/exam_review_slides_fall_2016.pdf 1066 2017 Percy Liang ai Stanford
154 Logic III https://web.stanford.edu/class/cs221/2017/lectures/logic3.pdf 1120 2017 Percy Liang ai Stanford
155 Conclusion https://web.stanford.edu/class/cs221/2017/lectures/conclusion.pdf 1066 2017 Percy Liang ai Stanford
156 Overview https://web.stanford.edu/class/cs221/2017/lectures/overview.pdf 1066 2017 Percy Liang ai Stanford
157 Deep Reinforcement Learning https://web.stanford.edu/class/cs221/2017/lectures/deep-rl.pdf 1336 2017 Percy Liang ai Stanford
158 Logic II https://web.stanford.edu/class/cs221/2017/lectures/logic2.pdf 1120 2017 Percy Liang ai Stanford
159 MDPs I https://web.stanford.edu/class/cs221/2017/lectures/mdp1.pdf 1331 2017 Percy Liang ai Stanford
160 Search II https://web.stanford.edu/class/cs221/2017/lectures/search2.pdf 1565 2017 Percy Liang ai Stanford
161 CSPs I https://web.stanford.edu/class/cs221/2017/lectures/csp1.pdf 1309 2017 Percy Liang ai Stanford
162 Bayesian networks II https://web.stanford.edu/class/cs221/2017/lectures/bayes2.pdf 1339 2017 Percy Liang ai Stanford
163 Search DP, UCS, A* https://web.stanford.edu/class/cs221/2017/lectures/section3.pdf 1565 2017 Percy Liang ai Stanford
164 Search I https://web.stanford.edu/class/cs221/2017/lectures/search1.pdf 1565 2017 Percy Liang ai Stanford
165 Machine Learning https://courseworks.columbia.edu/access/content/group/COMSW4701_002_2014_3/week6.pdf 1066 2014 Ansaf Salleb-Aouissi ai Columbia
166 Introduction https://courseworks.columbia.edu/access/content/group/COMSW4701_002_2014_3/week1.pdf 1066 2014 Ansaf Salleb-Aouissi ai Columbia
167 Adversarial Search https://courseworks.columbia.edu/access/content/group/COMSW4701_002_2014_3/week4.pdf 1306 2014 Ansaf Salleb-Aouissi ai Columbia
168 Logistic Regression https://courseworks.columbia.edu/access/content/group/COMSW4701_002_2014_3/week9_combo.pdf 1158 2014 Ansaf Salleb-Aouissi ai Columbia
169 Problem Solving:Search https://courseworks.columbia.edu/access/content/group/COMSW4701_002_2014_3/week3.pdf 1303 2014 Ansaf Salleb-Aouissi ai Columbia
170 Intelligent Agent (continued) https://courseworks.columbia.edu/access/content/group/COMSW4701_002_2014_3/week2.pdf 1312 2014 Ansaf Salleb-Aouissi ai Columbia
171 Linear Models https://courseworks.columbia.edu/access/content/group/COMSW4701_002_2014_3/week7.pdf 1160 2014 Ansaf Salleb-Aouissi ai Columbia
172 First Order Logic https://courseworks.columbia.edu/access/content/group/COMSW4701_002_2014_3/week13_FOL.pdf 1121 2014 Ansaf Salleb-Aouissi ai Columbia
173 SVM https://courseworks.columbia.edu/access/content/group/COMSW4701_002_2014_3/week10_svms.pdf 1161 2014 Ansaf Salleb-Aouissi ai Columbia
174 A* optimality Local Search https://courseworks.columbia.edu/access/content/group/COMSW4701_002_2014_3/week5_combo.pdf 1565 2014 Ansaf Salleb-Aouissi ai Columbia
175 Logical Agents https://courseworks.columbia.edu/access/content/group/COMSW4701_002_2014_3/week12_propositional_logic.pdf 1312 2014 Ansaf Salleb-Aouissi ai Columbia
176 Tree classifiers https://courseworks.columbia.edu/access/content/group/COMSW4701_002_2014_3/week9_trees.pdf 1153 2014 Ansaf Salleb-Aouissi ai Columbia
177 Unsupervised learning https://courseworks.columbia.edu/access/content/group/COMSW4701_002_2014_3/week11_combo.pdf 1203 2014 Ansaf Salleb-Aouissi ai Columbia
178 Planning https://www2.cs.duke.edu/courses/spring16/compsci270/slides/270_11.pdf 1143 2016 George Konidaris ai Duke
179 First-Order Logic https://www2.cs.duke.edu/courses/spring16/compsci270/slides/270_6.pdf 1121 2016 George Konidaris ai Duke
180 Machine Learning https://www2.cs.duke.edu/courses/spring16/compsci270/slides/270_14.pdf 1066 2016 George Konidaris ai Duke
181 Uncertainty https://www2.cs.duke.edu/courses/spring16/compsci270/slides/270_7.pdf 1329 2016 George Konidaris ai Duke
182 Unsupervised Learning https://www2.cs.duke.edu/courses/spring16/compsci270/slides/270_16.pdf 1203 2016 George Konidaris ai Duke
183 Introduction to Artificial Intelligence https://www2.cs.duke.edu/courses/spring16/compsci270/slides/270_1.pdf 1561 2016 George Konidaris ai Duke
184 Informed Search https://www2.cs.duke.edu/courses/spring16/compsci270/slides/270_3.pdf 1304 2016 George Konidaris ai Duke
185 Hidden Markov Models https://www2.cs.duke.edu/courses/spring16/compsci270/slides/270_10.pdf 1016 2016 George Konidaris ai Duke
186 GameTheory https://www2.cs.duke.edu/courses/spring16/compsci270/slides/game_theory.pdf 1307 2016 George Konidaris ai Duke
187 Search https://www2.cs.duke.edu/courses/spring16/compsci270/slides/270_2.pdf 1565 2016 George Konidaris ai Duke
188 Philosophical, Social, and Ethical Issues https://www2.cs.duke.edu/courses/spring16/compsci270/slides/270_18.pdf 1228 2016 George Konidaris ai Duke
189 Bayesian Networks II https://www2.cs.duke.edu/courses/spring16/compsci270/slides/270_9.pdf 1330 2016 George Konidaris ai Duke
190 Robot Motion Planning https://www2.cs.duke.edu/courses/spring16/compsci270/slides/270_13.pdf 1340 2016 George Konidaris ai Duke
191 Adversarial Search https://www2.cs.duke.edu/courses/spring16/compsci270/slides/270_4.pdf 1306 2016 George Konidaris ai Duke
192 Computing Game-Theoretic Solutions for Security https://www2.cs.duke.edu/courses/spring16/compsci270/slides/conitzer_guest_lecture.pdf 1307 2016 George Konidaris ai Duke
193 Python https://www2.cs.duke.edu/courses/spring16/compsci270/slides/python-static.pdf 1063 2016 George Konidaris ai Duke
194 Probabilistic Planning https://www2.cs.duke.edu/courses/spring16/compsci270/slides/270_12.pdf 1310 2016 George Konidaris ai Duke
195 Machine Learning https://www2.cs.duke.edu/courses/spring16/compsci270/slides/270_15.pdf 1066 2016 George Konidaris ai Duke
196 Knowledge Representation and Reasoning https://www2.cs.duke.edu/courses/spring16/compsci270/slides/270_5.pdf 1028 2016 George Konidaris ai Duke
197 Bayesian Networks https://www2.cs.duke.edu/courses/spring16/compsci270/slides/270_8.pdf 1330 2016 George Konidaris ai Duke
198 A*: Proof of Optimality https://www2.cs.duke.edu/courses/spring16/compsci270/slides/astar.pdf 1565 2016 George Konidaris ai Duke
199 Reinforcement Learning https://www2.cs.duke.edu/courses/spring16/compsci270/slides/270_17.pdf 1336 2016 George Konidaris ai Duke
200 Markov Decision Processes http://ai.berkeley.edu/slides/Lecture%208%20--%20MDPs%20I/SP14%20CS188%20Lecture%208%20--%20MDPs%20I.pptx 1331 2014 Dan Klein and Pieter Abbeel ai Berkeley
201 Advanced Applications: Computer Vision and Robotics * http://ai.berkeley.edu/slides/Lecture%2025%20--%20Advanced%20Applications%20(Computer%20Vision,%20Robotics)/SP14%20CS188%20Lecture%2025%20--%20Advanced%20Applications%20(Computer%20Vision%20and%20Robotics).pptx 1338 2014 Dan Klein and Pieter Abbeel ai Berkeley
202 Search http://ai.berkeley.edu/slides/Lecture%202%20--%20Uninformed%20Search/SP14%20CS188%20Lecture%202%20--%20Uninformed%20Search.pptx 1565 2014 Dan Klein and Pieter Abbeel ai Berkeley
203 Kernels and Clustering http://ai.berkeley.edu/slides/Lecture%2023%20--%20Kernels%20and%20Clustering/SP14%20CS188%20Lecture%2023%20--%20Kernels%20and%20Clustering.pptx 1167 2014 Dan Klein and Pieter Abbeel ai Berkeley
204 Informed Search http://ai.berkeley.edu/slides/Lecture%203%20--%20Informed%20Search/SP14%20CS188%20Lecture%203%20--%20Informed%20Search.pptx 1282 2014 Dan Klein and Pieter Abbeel ai Berkeley
205 Bayes Nets http://ai.berkeley.edu/slides/Lecture%2016%20--%20Bayes%20Nets%20I%20Representation/SP14%20CS188%20Lecture%2016%20--%20Bayes%20Nets.pptx 1155 2014 Dan Klein and Pieter Abbeel ai Berkeley
206 Particle Filters and Applications of HMMs http://ai.berkeley.edu/slides/Lecture%2015%20--%20Particle%20Filters%20and%20Applications%20of%20HMMs/SP14%20CS188%20Lecture%2015%20--%20Particle%20Filters%20and%20Applications%20of%20HMMs.pptx 1086 2014 Dan Klein and Pieter Abbeel ai Berkeley
207 Constraint Satisfaction Problems II http://ai.berkeley.edu/slides/Lecture%205%20--%20CSPs%20II/SP14%20CS188%20Lecture%205%20--%20CSPs%20II.pptx 1167 2014 Dan Klein and Pieter Abbeel ai Berkeley
208 Bayes Nets: Inference http://ai.berkeley.edu/slides/Lecture%2018%20--%20Bayes%20Nets%20III%20Inference/SP14%20cs188%20Lecture%2018%20--%20Bayes%20Nets%20III%20Inference.pptx 1330 2014 Dan Klein and Pieter Abbeel ai Berkeley
209 Markov Models http://ai.berkeley.edu/slides/Lecture%2013%20--%20Markov%20Models/SP14%20CS188%20Lecture%2013%20--%20Markov%20Models.pptx 1016 2014 Dan Klein and Pieter Abbeel ai Berkeley
210 Perceptrons http://ai.berkeley.edu/slides/Lecture%2022%20--%20Perceptron/SP14%20CS188%20Lecture%2022%20--%20Perceptron.pptx 1157 2014 Dan Klein and Pieter Abbeel ai Berkeley
211 Uncertainty and Utilities http://ai.berkeley.edu/slides/Lecture%207%20--%20Expectimax%20Search%20and%20Utilities/SP14%20CS188%20Lecture%207%20--%20Expectimax%20Search%20and%20Utilities.pptx 1329 2014 Dan Klein and Pieter Abbeel ai Berkeley
212 Hidden Markov Models http://ai.berkeley.edu/slides/Lecture%2014%20--%20HMMs/SP14%20CS188%20Lecture%2014%20--%20Hidden%20Markov%20Models.pptx 1016 2014 Dan Klein and Pieter Abbeel ai Berkeley
213 Bayes Nets: Independence http://ai.berkeley.edu/slides/Lecture%2017%20--%20Bayes%20Nets%20II%20Independence/SP14%20CS188%20Lecture%2017%20--%20Bayes%20Nets%20II%20Independence.pptx 1330 2014 Dan Klein and Pieter Abbeel ai Berkeley
214 Decision Networks and Value of Perfect Information http://ai.berkeley.edu/slides/Lecture%2020%20--%20Decision%20Diagrams%20and%20Value%20of%20Perfect%20Information/SP14%20CS188%20Lecture%2020%20--%20Decision%20Diagrams%20and%20VPI.pptx 1044 2014 Dan Klein and Pieter Abbeel ai Berkeley
215 Introduction http://ai.berkeley.edu/slides/Lecture%201%20--%20Introduction/SP14%20CS188%20Lecture%201%20--%20Introduction.pptx 1561 2014 Dan Klein and Pieter Abbeel ai Berkeley
216 Probability http://ai.berkeley.edu/slides/Lecture%2012%20--%20Probability/SP14%20CS188%20Lecture%2012%20--%20Probability.pptx 1555 2014 Dan Klein and Pieter Abbeel ai Berkeley
217 Adversarial Search http://ai.berkeley.edu/slides/Lecture%206%20--%20Adversarial%20Search/SP14%20CS188%20Lecture%206%20--%20Adversarial%20Search.pptx 1306 2014 Dan Klein and Pieter Abbeel ai Berkeley
218 CSPs http://ai.berkeley.edu/slides/Lecture%204%20--%20CSPs%20I/SP14%20CS188%20Lecture%204%20--%20CSPs%20I.pptx 1309 2014 Dan Klein and Pieter Abbeel ai Berkeley
219 Naive Bayes http://ai.berkeley.edu/slides/Lecture%2021%20--%20Naive%20Bayes/SP14%20CS188%20Lecture%2021%20--%20Naive%20Bayes.pptx 1060 2014 Dan Klein and Pieter Abbeel ai Berkeley
220 Reinforcement Learning I http://ai.berkeley.edu/slides/Lecture%2010%20--%20Reinforcement%20Learning%20I/SP14%20CS188%20Lecture%2010%20--%20Reinforcement%20Learning%20I.pptx 1366 2014 Dan Klein and Pieter Abbeel ai Berkeley
221 Reinforcement Learning II http://ai.berkeley.edu/slides/Lecture%2011%20--%20Reinforcement%20Learning%20II/SP14%20CS188%20Lecture%2011%20--%20Reinforcement%20Learning%20II.pptx 1366 2014 Dan Klein and Pieter Abbeel ai Berkeley
222 Markov Decision Processes II http://ai.berkeley.edu/slides/Lecture%209%20--%20MDPs%20II/SP14%20CS188%20Lecture%209%20--%20MDPs%20II.pptx 1331 2014 Dan Klein and Pieter Abbeel ai Berkeley
223 Bayes Nets: Sampling http://ai.berkeley.edu/slides/Lecture%2019%20--%20Bayes%20Net%20IV%20Sampling/SP14%20CS188%20Lecture%2019%20--%20Bayes%20Nets%20IV%20Sampling.pptx 1330 2014 Dan Klein and Pieter Abbeel ai Berkeley
224 Uncertainty http://pages.cs.wisc.edu/~dyer/cs540/notes/12_uncertainty.pdf 1329 2018 Chuck Dyer ai University of Wisconsin-Madison
225 Introduction http://pages.cs.wisc.edu/~dyer/cs540/notes/01_intro.pdf 1561 2018 Chuck Dyer ai University of Wisconsin-Madison
226 Game Playing http://pages.cs.wisc.edu/~dyer/cs540/notes/06_gamePlaying.pdf 1307 2018 Chuck Dyer ai University of Wisconsin-Madison
227 Bayesian Networks http://pages.cs.wisc.edu/~dyer/cs540/notes/13_bayes-net.pdf 1330 2018 Chuck Dyer ai University of Wisconsin-Madison
228 Supervised Learning Methods k-nearest-neighbors(k-NN) Decisiontrees(Chapter18.3) Neuralnetworks(ANN) Supportvectormachines(SVM) http://pages.cs.wisc.edu/~dyer/cs540/notes/11_learning-decision-trees.pdf 1153 2018 Chuck Dyer ai University of Wisconsin-Madison
229 Supervised Learning Methods k-nearest-neighbors (k-NN) Decision trees Support vector machines (SVM) Neural networks http://pages.cs.wisc.edu/~dyer/cs540/notes/10_svm.pdf 1153 2018 Chuck Dyer ai University of Wisconsin-Madison
230 Face Detection and Recognition I http://pages.cs.wisc.edu/~dyer/cs540/notes/16.0_face-detection.pdf 1338 2018 Chuck Dyer ai University of Wisconsin-Madison
231 Introduction http://pages.cs.wisc.edu/~dyer/cs540/notes/09_neuralNets.pdf 1561 2018 Chuck Dyer ai University of Wisconsin-Madison
232 Informed Search http://pages.cs.wisc.edu/~dyer/cs540/notes/04_informedSearch.pdf 1304 2018 Chuck Dyer ai University of Wisconsin-Madison
233 Face Detection and Recognition II http://pages.cs.wisc.edu/~dyer/cs540/notes/16.1_face-recognition.pdf 1338 2018 Chuck Dyer ai University of Wisconsin-Madison
234 Constraint Satisfaction Problems http://pages.cs.wisc.edu/~dyer/cs540/notes/07_csp.pdf 1309 2018 Chuck Dyer ai University of Wisconsin-Madison
235 Speech Recognition http://pages.cs.wisc.edu/~dyer/cs540/notes/14_speech.pdf 1542 2018 Chuck Dyer ai University of Wisconsin-Madison
236 Machine Learning: Introduction and Unsupervised Learning http://pages.cs.wisc.edu/~dyer/cs540/notes/08_learning-intro.pdf 1066 2018 Chuck Dyer ai University of Wisconsin-Madison
237 Genetic Algorithms http://pages.cs.wisc.edu/~dyer/cs540/notes/05_geneticAlgorithms.pdf 1308 2018 Chuck Dyer ai University of Wisconsin-Madison
238 Uninformed Search http://pages.cs.wisc.edu/~dyer/cs540/notes/03_uninformedSearch.pdf 1304 2018 Chuck Dyer ai University of Wisconsin-Madison
239 Invariant Theory http://ttic.uchicago.edu/~dmcallester/DeepClass18/03CNNs/CNNb.pdf 1066 2017 David McAllester dl Chicago
240 Deep Graphical Models III http://ttic.uchicago.edu/~dmcallester/DeepClass18/09GraphicalModels/DGMs3.pdf 1330 2017 David McAllester dl Chicago
241 Variational Autoencoders http://ttic.uchicago.edu/~dmcallester/DeepClass18/11AutoEncoders/Variational.pdf 1181 2017 David McAllester dl Chicago
242 Deep Graphical Models II http://ttic.uchicago.edu/~dmcallester/DeepClass18/09GraphicalModels/DGMs2.pdf 1181 2017 David McAllester dl Chicago
243 Gradients as Dual Vectors http://ttic.uchicago.edu/~dmcallester/DeepClass18/13SGD2/loose.pdf 1181 2017 David McAllester dl Chicago
244 Gated RNNs http://ttic.uchicago.edu/~dmcallester/DeepClass18/04Highway/highway.pdf 1190 2017 David McAllester dl Chicago
245 Introduction and Historical Notes http://ttic.uchicago.edu/~dmcallester/DeepClass18/01intro/intro.pdf 1181 2017 David McAllester dl Chicago
246 Language Modeling http://ttic.uchicago.edu/~dmcallester/DeepClass18/05RNNs/RNN.pdf 1015 2017 David McAllester dl Chicago
247 Information Theory http://ttic.uchicago.edu/~dmcallester/DeepClass18/08InfoTheory/information.pdf 1042 2017 David McAllester dl Chicago
248 Rate-Distortion Autoencoders http://ttic.uchicago.edu/~dmcallester/DeepClass18/11AutoEncoders/Rate.pdf 1181 2017 David McAllester dl Chicago
249 Multiclass Logistic Regression http://ttic.uchicago.edu/~dmcallester/DeepClass18/02MLP/MLPa.pdf 1158 2017 David McAllester dl Chicago
250 Stochastic Gradient Descent http://ttic.uchicago.edu/~dmcallester/DeepClass18/06SGD/SGD.pdf 1320 2017 David McAllester dl Chicago
251 AlphaZero http://ttic.uchicago.edu/~dmcallester/DeepClass18/16alpha/alphago.pdf 1307 2017 David McAllester dl Chicago
252 Computation Graphs and Backpropagation http://ttic.uchicago.edu/~dmcallester/DeepClass18/02MLP/MLPb.pdf 1181 2017 David McAllester dl Chicago
253 Convolutional Neural Networks — CNNs http://ttic.uchicago.edu/~dmcallester/DeepClass18/03CNNs/CNNs.pdf 1193 2017 David McAllester dl Chicago
254 In Search of AGI http://ttic.uchicago.edu/~dmcallester/DeepClass18/18AGI/arch.pdf 1300 2017 David McAllester dl Chicago
255 Deep Reinforcement Learning http://ttic.uchicago.edu/~dmcallester/DeepClass18/15RL/RL.pdf 1189 2017 David McAllester dl Chicago
256 Regularization http://ttic.uchicago.edu/~dmcallester/DeepClass18/07regularization/regularization.pdf 1336 2017 David McAllester dl Chicago
257 Deep Graphical Models I http://ttic.uchicago.edu/~dmcallester/DeepClass18/09GraphicalModels/DGMs.pdf 1181 2017 David McAllester dl Chicago
258 Generative Adversarial Networks (GANs) http://ttic.uchicago.edu/~dmcallester/DeepClass18/14GANs/Gans.pdf 1151 2017 David McAllester dl Chicago
259 Memory Networks for Language Understanding http://uvadlc.github.io/lectures/lecture11-jasonweston.pdf 1194 2018 Efstratios Gavves dl GitHub
260 Deep Learning on Graph-Structured Data http://uvadlc.github.io/lectures/lecture10.pdf 1315 2018 Efstratios Gavves dl GitHub
261 Exotic Deep Learning http://uvadlc.github.io/lectures/lecture14.pdf 1181 2018 Efstratios Gavves dl GitHub
262 Learning with neural networks http://uvadlc.github.io/lectures/lecture2.pdf 1181 2018 Efstratios Gavves dl GitHub
263 Unsupervised, Generative & Adversarial Networks http://uvadlc.github.io/lectures/lecture9.pdf 1181 2018 Efstratios Gavves dl GitHub
264 Deeper into Deep Learning and Optimizations http://uvadlc.github.io/lectures/lecture3.pdf 1320 2018 Efstratios Gavves dl GitHub
265 Understanding Convnets and Knowledge Transfer http://uvadlc.github.io/lectures/lecture5.pdf 1193 2018 Efstratios Gavves dl GitHub
266 Recurrent Neural Networks http://uvadlc.github.io/lectures/lecture8.pdf 1192 2018 Efstratios Gavves dl GitHub
267 Deep Learning & HPC http://uvadlc.github.io/lectures/surfsara-slides.pdf 1181 2018 Efstratios Gavves dl GitHub
268 Structured Prediction with ConvNets http://uvadlc.github.io/lectures/lecture6.pdf 1192 2018 Efstratios Gavves dl GitHub
269 Introduction to Neural Networks and Deep Learning http://uvadlc.github.io/lectures/lecture1.pdf 1181 2018 Efstratios Gavves dl GitHub
270 Deep Learning Language Models and Word Embeddings http://uvadlc.github.io/lectures/lecture7-christofmonz.pdf 1115 2018 Efstratios Gavves dl GitHub
271 Convolutional Neural Networks for Computer Vision http://uvadlc.github.io/lectures/lecture4.pdf 1195 2018 Efstratios Gavves dl GitHub
272 Autoencoder - denoising autoencoder http://info.usherbrooke.ca/hlarochelle/ift725/6_06_denoising_autoencoder.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
273 Training CRFs - general conditional random fields http://info.usherbrooke.ca/hlarochelle/ift725/4_07_general_crf.pdf 1091 2017 Hugo Larochelle dl Université de Sherbrooke
274 Conditional random fields (CRFs) http://info.usherbrooke.ca/hlarochelle/ift725/crf.pdf 1091 2017 Hugo Larochelle dl Université de Sherbrooke
275 Restricted Boltzmann machine http://info.usherbrooke.ca/hlarochelle/ift725/5_01_definition.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
276 Training neural networks - activation function derivative http://info.usherbrooke.ca/hlarochelle/ift725/2_05_activation_function_derivative.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
277 Sparse coding - dictionary learning algorithm http://info.usherbrooke.ca/hlarochelle/ift725/8_05_dictionary_learning_algorithm.pdf 1183 2017 Hugo Larochelle dl Université de Sherbrooke
278 Computer vision - pooling and subsampling http://info.usherbrooke.ca/hlarochelle/ift725/9_05_pooling_and_subsampling.pdf 1338 2017 Hugo Larochelle dl Université de Sherbrooke
279 Natural language processing - multitask learning http://info.usherbrooke.ca/hlarochelle/ift725/10_10_multitask_learning.pdf 1183 2017 Hugo Larochelle dl Université de Sherbrooke
280 Autoencoder - linear autoencoder http://info.usherbrooke.ca/hlarochelle/ift725/6_04_linear_autoencoder.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
281 Restricted Boltzmann machine - persistent CD http://info.usherbrooke.ca/hlarochelle/ift725/5_06_persistent_CD.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
282 Computer vision - convolutional network http://info.usherbrooke.ca/hlarochelle/ift725/9_06_convolutional_network.pdf 1195 2017 Hugo Larochelle dl Université de Sherbrooke
283 Training CRFs - discriminative vs. generative learning http://info.usherbrooke.ca/hlarochelle/ift725/4_04_discriminative_vs_generative.pdf 1091 2017 Hugo Larochelle dl Université de Sherbrooke
284 Computer vision - example http://info.usherbrooke.ca/hlarochelle/ift725/9_08_example.pdf 1338 2017 Hugo Larochelle dl Université de Sherbrooke
285 Autoencoder - example http://info.usherbrooke.ca/hlarochelle/ift725/6_03_example.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
286 Sparse coding - ZCA preprocessing http://info.usherbrooke.ca/hlarochelle/ift725/8_07_ZCA_preprocessing.pdf 1183 2017 Hugo Larochelle dl Université de Sherbrooke
287 Training CRFs http://info.usherbrooke.ca/hlarochelle/ift725/crf_learn.pdf 1091 2017 Hugo Larochelle dl Université de Sherbrooke
288 Training neural networks - optimization http://info.usherbrooke.ca/hlarochelle/ift725/2_11_optimization.pdf 1320 2017 Hugo Larochelle dl Université de Sherbrooke
289 Training CRFs - hidden Markov model http://info.usherbrooke.ca/hlarochelle/ift725/4_06_hidden_markov_model.pdf 1187 2017 Hugo Larochelle dl Université de Sherbrooke
290 Training neural networks - hidden layer gradient http://info.usherbrooke.ca/hlarochelle/ift725/2_04_hidden_layer_gradient.pdf 1182 2017 Hugo Larochelle dl Université de Sherbrooke
291 Word representations http://info.usherbrooke.ca/hlarochelle/ift725/nlp-word-representations.pdf 1186 2017 Hugo Larochelle dl Université de Sherbrooke
292 Recursive neural network http://info.usherbrooke.ca/hlarochelle/ift725/nlp-recursive-net.pdf 1192 2017 Hugo Larochelle dl Université de Sherbrooke
293 Training CRFs - loss function http://info.usherbrooke.ca/hlarochelle/ift725/4_01_loss_function.pdf 1091 2017 Hugo Larochelle dl Université de Sherbrooke
294 Training CRFs - pseudo likelihood http://info.usherbrooke.ca/hlarochelle/ift725/4_08_pseudolikelihood.pdf 1191 2017 Hugo Larochelle dl Université de Sherbrooke
295 Merging representations http://info.usherbrooke.ca/hlarochelle/ift725/10_12_merging_representations.pdf 1114 2017 Hugo Larochelle dl Université de Sherbrooke
296 Feedforward neural network - artificial neural network http://info.usherbrooke.ca/hlarochelle/ift725/1_01_artificial_neuron.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
297 Recursive network training http://info.usherbrooke.ca/hlarochelle/ift725/10_14_recursive_network_training.pdf 1192 2017 Hugo Larochelle dl Université de Sherbrooke
298 Restricted Boltzmann machine - free energy http://info.usherbrooke.ca/hlarochelle/ift725/5_03_free_energy.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
299 Restricted Boltzmann machine - contrastive divergence http://info.usherbrooke.ca/hlarochelle/ift725/5_04_contrastive_divergence.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
300 Conditional random fields - context window http://info.usherbrooke.ca/hlarochelle/ift725/3_03_context_window.pdf 1091 2017 Hugo Larochelle dl Université de Sherbrooke
301 Deep learning - unsupervised pre-training http://info.usherbrooke.ca/hlarochelle/ift725/7_03_unsupervised_pretraining.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
302 Deep learning - deep autoencoder http://info.usherbrooke.ca/hlarochelle/ift725/7_06_deep_autoencoder.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
303 Training neural networks - empirical risk minimization http://info.usherbrooke.ca/hlarochelle/ift725/2_01_empirical_risk_minimization.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
304 Deep learning - dropout http://info.usherbrooke.ca/hlarochelle/ift725/7_05_dropout.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
305 Autoencoders http://info.usherbrooke.ca/hlarochelle/ift725/autoencoder.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
306 Training neural networks - parameter gradient http://info.usherbrooke.ca/hlarochelle/ift725/2_06_parameter_gradient.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
307 Conditional random fields - performing classification http://info.usherbrooke.ca/hlarochelle/ift725/3_06_performing_classification.pdf 1091 2017 Hugo Larochelle dl Université de Sherbrooke
308 Training neural networks - parameter initialization http://info.usherbrooke.ca/hlarochelle/ift725/2_09_parameter_initialization.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
309 Training neural networks http://info.usherbrooke.ca/hlarochelle/ift725/bprop.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
310 Training CRFs - unary log-factor gradient http://info.usherbrooke.ca/hlarochelle/ift725/4_02_unary_log-factor_gradient.pdf 1090 2017 Hugo Larochelle dl Université de Sherbrooke
311 Feedforward neural network - multilayer neural network http://info.usherbrooke.ca/hlarochelle/ift725/1_04_multilayer_neural_network.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
312 Syntactic and semantic tagging http://info.usherbrooke.ca/hlarochelle/ift725/nlp-tagging.pdf 1088 2017 Hugo Larochelle dl Université de Sherbrooke
313 Natural language processing - one-hot encoding http://info.usherbrooke.ca/hlarochelle/ift725/10_03_one-hot_encoding.pdf 1114 2017 Hugo Larochelle dl Université de Sherbrooke
314 Training neural networks - regularization http://info.usherbrooke.ca/hlarochelle/ift725/2_08_regularization.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
315 Autoencoder - loss function http://info.usherbrooke.ca/hlarochelle/ift725/6_02_loss_function.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
316 Sparse coding - definition http://info.usherbrooke.ca/hlarochelle/ift725/8_01_definition.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
317 Feedforward neural network - capacity of single neuron http://info.usherbrooke.ca/hlarochelle/ift725/1_03_capacity_of_single_neuron.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
318 Conditional random fields - belief propagation http://info.usherbrooke.ca/hlarochelle/ift725/3_10_belief_propagation.pdf 1091 2017 Hugo Larochelle dl Université de Sherbrooke
319 Natural language processing - word tagging http://info.usherbrooke.ca/hlarochelle/ift725/10_08_word_tagging.pdf 1088 2017 Hugo Larochelle dl Université de Sherbrooke
320 Neural networks Autoencoder - undercomplete vs. overcomplete hidden layer http://info.usherbrooke.ca/hlarochelle/ift725/6_05_undercomplete_vs_overcomplete_hidden_layer.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
321 Training neural networks - backpropagation algorithm http://info.usherbrooke.ca/hlarochelle/ift725/2_07_backpropagation.pdf 1320 2017 Hugo Larochelle dl Université de Sherbrooke
322 Feedforward neural network http://info.usherbrooke.ca/hlarochelle/ift725/fprop.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
323 Feedforward neural network - biological inspiration http://info.usherbrooke.ca/hlarochelle/ift725/1_06_biological_inspiration.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
324 Computer vision - discrete convolution http://info.usherbrooke.ca/hlarochelle/ift725/9_04_discrete_convolution.pdf 1193 2017 Hugo Larochelle dl Université de Sherbrooke
325 Neural network language model http://info.usherbrooke.ca/hlarochelle/ift725/nlp-language-model.pdf 1195 2017 Hugo Larochelle dl Université de Sherbrooke
326 Training neural networks - loss function http://info.usherbrooke.ca/hlarochelle/ift725/2_02_loss_function.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
327 Restricted Boltzmann machine - inference http://info.usherbrooke.ca/hlarochelle/ift725/5_02_inference.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
328 Restricted Boltzmann machine - contrastive divergence (parameter update) http://info.usherbrooke.ca/hlarochelle/ift725/5_05_contrastive_divergence_parameter_update.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
329 Deep learning - deep belief network http://info.usherbrooke.ca/hlarochelle/ift725/7_07_deep_belief_network.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
330 Language modeling http://info.usherbrooke.ca/hlarochelle/ift725/10_05_language_modeling.pdf 1005 2017 Hugo Larochelle dl Université de Sherbrooke
331 Training CRFs - pairwise log-factor gradient http://info.usherbrooke.ca/hlarochelle/ift725/4_03_pairwise_log-factor_gradient.pdf 1090 2017 Hugo Larochelle dl Université de Sherbrooke
332 Restricted Boltzmann machine - example http://info.usherbrooke.ca/hlarochelle/ift725/5_07_example.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
333 Conditional random fields - linear chain CRF http://info.usherbrooke.ca/hlarochelle/ift725/3_02_linear_chain_crf.pdf 1090 2017 Hugo Larochelle dl Université de Sherbrooke
334 Computer vision http://info.usherbrooke.ca/hlarochelle/ift725/convolutional_network.pdf 1338 2017 Hugo Larochelle dl Université de Sherbrooke
335 Deep learning http://info.usherbrooke.ca/hlarochelle/ift725/deep.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
336 language model http://info.usherbrooke.ca/hlarochelle/ift725/10_06_neural_network_language_model.pdf 1195 2017 Hugo Larochelle dl Université de Sherbrooke
337 Computer vision - data set expansion http://info.usherbrooke.ca/hlarochelle/ift725/9_09_data_set_expansion.pdf 1338 2017 Hugo Larochelle dl Université de Sherbrooke
338 Sparse coding - dictionary update with projected gradient descent http://info.usherbrooke.ca/hlarochelle/ift725/8_03_dictionary_update_projected_gradient_descent.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
339 Deep learning - variational bound http://info.usherbrooke.ca/hlarochelle/ift725/7_08_variational_bound.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
340 Natural language processing - recursive network http://info.usherbrooke.ca/hlarochelle/ift725/10_11_recursive_network.pdf 1192 2017 Hugo Larochelle dl Université de Sherbrooke
341 Deep learning - DBN pretraining http://info.usherbrooke.ca/hlarochelle/ift725/7_09_dbn_pretraining.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
342 Natural language processing - convolutional network http://info.usherbrooke.ca/hlarochelle/ift725/10_09_convolutional_network.pdf 1193 2017 Hugo Larochelle dl Université de Sherbrooke
343 Deep learning - difculty of training http://info.usherbrooke.ca/hlarochelle/ift725/7_02_difficulty_of_training.pdf 1182 2017 Hugo Larochelle dl Université de Sherbrooke
344 Training neural networks - output layer gradient http://info.usherbrooke.ca/hlarochelle/ift725/2_03_output_layer_gradient.pdf 1182 2017 Hugo Larochelle dl Université de Sherbrooke
345 Computer vision - motivation http://info.usherbrooke.ca/hlarochelle/ift725/9_01.motivation.pdf 1338 2017 Hugo Larochelle dl Université de Sherbrooke
346 Natural language processing - hierarchical output layer http://info.usherbrooke.ca/hlarochelle/ift725/10_07_hierarchical_output_layer.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
347 Sparse coding - online dictionary learning algorithm http://info.usherbrooke.ca/hlarochelle/ift725/8_06_online_dictionary_learning_algorithm.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
348 Sparse coding http://info.usherbrooke.ca/hlarochelle/ift725/sparse_coding.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
349 Review of fundamentals http://info.usherbrooke.ca/hlarochelle/ift725/review.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
350 Deep learning - example http://info.usherbrooke.ca/hlarochelle/ift725/7_04_example.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
351 Conditional random fields - Markov network http://info.usherbrooke.ca/hlarochelle/ift725/3_08_markov_network.pdf 1091 2017 Hugo Larochelle dl Université de Sherbrooke
352 Restricted Boltzmann machine - extensions http://info.usherbrooke.ca/hlarochelle/ift725/5_08_extensions.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
353 Feedforward neural network - activation function http://info.usherbrooke.ca/hlarochelle/ift725/1_02_activation_function.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
354 Computer vision - convolutional RBM http://info.usherbrooke.ca/hlarochelle/ift725/9_10_convolutional_rbm.pdf 1338 2017 Hugo Larochelle dl Université de Sherbrooke
355 Autoencoder - definition http://info.usherbrooke.ca/hlarochelle/ift725/6_01_definition.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
356 Conditional random fields - factor graph http://info.usherbrooke.ca/hlarochelle/ift725/3_09_factor_graph.pdf 1191 2017 Hugo Larochelle dl Université de Sherbrooke
357 Deep learning - motivation http://info.usherbrooke.ca/hlarochelle/ift725/7_01_motivation.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
358 Computer vision - parameter sharing http://info.usherbrooke.ca/hlarochelle/ift725/9_03_parameter_sharing.pdf 1338 2017 Hugo Larochelle dl Université de Sherbrooke
359 Feed-forward neural network - capacity of neural network http://info.usherbrooke.ca/hlarochelle/ift725/1_05_capacity_of_neural_network.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
360 Natural language processing - preprocessing http://info.usherbrooke.ca/hlarochelle/ift725/10_02_preprocessing.pdf 1073 2017 Hugo Larochelle dl Université de Sherbrooke
361 Computer vision - object recognition http://info.usherbrooke.ca/hlarochelle/ift725/9_07_object_recognition.pdf 1338 2017 Hugo Larochelle dl Université de Sherbrooke
362 Sparse coding http://info.usherbrooke.ca/hlarochelle/ift725/8_09_relationship_with_V1.pdf 1066 2017 Hugo Larochelle dl Université de Sherbrooke
363 Sparse coding - feature extraction http://info.usherbrooke.ca/hlarochelle/ift725/8_08_feature_extraction.pdf 1066 2017 Hugo Larochelle dl Université de Sherbrooke
364 Natural language processing - tree inference http://info.usherbrooke.ca/hlarochelle/ift725/10_13_tree_inference.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
365 Neural networks Conditional random elds - motivation http://info.usherbrooke.ca/hlarochelle/ift725/3_01_motivation.pdf 1059 2017 Hugo Larochelle dl Université de Sherbrooke
366 Natural language processing - motivation http://info.usherbrooke.ca/hlarochelle/ift725/10_01_motivation.pdf 1201 2017 Hugo Larochelle dl Université de Sherbrooke
367 Sparse coding - dictionary update with block-coordinate descent http://info.usherbrooke.ca/hlarochelle/ift725/8_04_dictionary_update_block-coordinate_descent.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
368 Restricted Boltzmann Machines http://info.usherbrooke.ca/hlarochelle/ift725/rbm.pdf 1066 2017 Hugo Larochelle dl Université de Sherbrooke
369 Training CRFs - maximum-entropy Markov model http://info.usherbrooke.ca/hlarochelle/ift725/4_05_maximum-entropy_markov_model.pdf 1091 2017 Hugo Larochelle dl Université de Sherbrooke
370 Computer vision - local connectivity http://info.usherbrooke.ca/hlarochelle/ift725/9_02_local_connectivity.pdf 1338 2017 Hugo Larochelle dl Université de Sherbrooke
371 Training neural networks - model selection http://info.usherbrooke.ca/hlarochelle/ift725/2_10_model_selection.pdf 1066 2017 Hugo Larochelle dl Université de Sherbrooke
372 Conditional random fields - computing marginals http://info.usherbrooke.ca/hlarochelle/ift725/3_05_computing_marginals.pdf 1091 2017 Hugo Larochelle dl Université de Sherbrooke
373 Conditional random fields - computing the partition function http://info.usherbrooke.ca/hlarochelle/ift725/3_04_computing_partition_function.pdf 1091 2017 Hugo Larochelle dl Université de Sherbrooke
374 Autoencoder - contractive autoencoder http://info.usherbrooke.ca/hlarochelle/ift725/6_07_contractive_autoencoder.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
375 Neural networks Conditional random elds - factors, sufcient statistics and linear CRF http://info.usherbrooke.ca/hlarochelle/ift725/3_07_factors_sufficient_statistics_linear_crf.pdf 1057 2017 Hugo Larochelle dl Université de Sherbrooke
376 Sparse coding - inference (ISTA algorithm) http://info.usherbrooke.ca/hlarochelle/ift725/8_02_inference_ISTA_algorithm.pdf 1181 2017 Hugo Larochelle dl Université de Sherbrooke
377 Natural language processing - word representations http://info.usherbrooke.ca/hlarochelle/ift725/10_04_word_representations.pdf 1114 2017 Hugo Larochelle dl Université de Sherbrooke
378 Faster ML Development with TensorFlow http://introtodeeplearning.com/materials/2018_6S191_Lecture8.pdf 1189 2018 Alexander Amini dl MIT
379 Deep Learning Limitations and New Frontiers http://introtodeeplearning.com/materials/2018_6S191_Lecture6.pdf 1181 2018 Alexander Amini dl MIT
380 Deep Generative Models http://introtodeeplearning.com/materials/2018_6S191_Lecture4.pdf 1181 2018 Alexander Amini dl MIT
381 Introduction to Deep Learning http://introtodeeplearning.com/materials/2018_6S191_Lecture1.pdf 1181 2018 Alexander Amini dl MIT
382 Deep Learning for Computer Vision http://introtodeeplearning.com/materials/2018_6S191_Lecture3.pdf 1338 2018 Alexander Amini dl MIT
383 Sequence Modeling with Neural Networks http://introtodeeplearning.com/materials/2018_6S191_Lecture2.pdf 1182 2018 Alexander Amini dl MIT
384 Introduction To Deep Learning http://introtodeeplearning.com/materials/2018_6S191_Lecture9.pdf 1181 2018 Alexander Amini dl MIT
385 Learning+Reasoning http://introtodeeplearning.com/materials/2018_6S191_Lecture10.pdf 1566 2018 Alexander Amini dl MIT
386 Deep Reinforcement Learning http://introtodeeplearning.com/materials/2018_6S191_Lecture5.pdf 1336 2018 Alexander Amini dl MIT
387 Introduction to Machine Learning http://www.cs.umd.edu/class/spring2018/cmsc422-0101/slides0201/lecture_20.pdf 1066 2018 Furong Huang ml UMD
388 Review and Practice Problems http://www.cs.umd.edu/class/spring2018/cmsc422-0101/slides0201/lecture_11.pdf 1066 2018 Furong Huang ml UMD
389 Geometry and Nearest Neighbors http://www.cs.umd.edu/class/spring2018/cmsc422-0101/slides0201/lecture_04.pdf 1153 2018 Furong Huang ml UMD
390 (Sub)gradient Descent http://www.cs.umd.edu/class/spring2018/cmsc422-0101/slides0201/lecture_14.pdf 1320 2018 Furong Huang ml UMD
391 The Perceptron http://www.cs.umd.edu/class/spring2018/cmsc422-0101/slides0201/lecture_06.pdf 1157 2018 Furong Huang ml UMD
392 Neural Networks II http://www.cs.umd.edu/class/spring2018/cmsc422-0101/slides0201/lecture_19.pdf 1181 2018 Furong Huang ml UMD
393 Introduction to Machine Learning http://www.cs.umd.edu/class/spring2018/cmsc422-0101/slides0201/lecture_01.pdf 1066 2018 Furong Huang ml UMD
394 Unsupervised Learning Principal Component Analysis http://www.cs.umd.edu/class/spring2018/cmsc422-0101/slides0201/lecture_17.pdf 1026 2018 Furong Huang ml UMD
395 Decision Trees & Limits of Learning http://www.cs.umd.edu/class/spring2018/cmsc422-0101/slides0201/lecture_03.pdf 1066 2018 Furong Huang ml UMD
396 A Probabilistic View of Machine Learning I http://www.cs.umd.edu/class/spring2018/cmsc422-0101/slides0201/lecture_15.pdf 1066 2018 Furong Huang ml UMD
397 Practical Issues: Features, Evaluation, Debugging http://www.cs.umd.edu/class/spring2018/cmsc422-0101/slides0201/lecture_08.pdf 1066 2018 Furong Huang ml UMD
398 Imbalanced Data and Reductions http://www.cs.umd.edu/class/spring2018/cmsc422-0101/slides0201/lecture_09.pdf 1066 2018 Furong Huang ml UMD
399 K-Means Clustering (Unsupervised Learning) http://www.cs.umd.edu/class/spring2018/cmsc422-0101/slides0201/lecture_05.pdf 1167 2018 Furong Huang ml UMD
400 Bias and Fairness http://www.cs.umd.edu/class/spring2018/cmsc422-0101/slides0201/lecture_12.pdf 1066 2018 Furong Huang ml UMD
401 Decision Trees http://www.cs.umd.edu/class/spring2018/cmsc422-0101/slides0201/lecture_02.pdf 1066 2018 Furong Huang ml UMD
402 A Probabilistic View of Machine Learning II http://www.cs.umd.edu/class/spring2018/cmsc422-0101/slides0201/lecture_16.pdf 1066 2018 Furong Huang ml UMD
403 Neural Networks I http://www.cs.umd.edu/class/spring2018/cmsc422-0101/slides0201/lecture_18.pdf 1181 2018 Furong Huang ml UMD
404 The Perceptron http://www.cs.umd.edu/class/spring2018/cmsc422-0101/slides0201/lecture_07.pdf 1157 2018 Furong Huang ml UMD
405 Deep Learning II http://www.cs.umd.edu/class/spring2018/cmsc422-0101/slides0201/lecture_21.pdf 1181 2018 Furong Huang ml UMD
406 Multiclass Classification and Reductions http://www.cs.umd.edu/class/spring2018/cmsc422-0101/slides0201/lecture_10.pdf 1153 2018 Furong Huang ml UMD
407 Binary Classification with Linear Models http://www.cs.umd.edu/class/spring2018/cmsc422-0101/slides0201/lecture_13.pdf 1153 2018 Furong Huang ml UMD
408 Principal components analysis http://www.columbia.edu/~jwp2128/Teaching/W4721/Spring2017/slides/lecture_4-6-17.pdf 1026 2017 John Paisley ml Columbia
409 Unsupervised Learning http://www.columbia.edu/~jwp2128/Teaching/W4721/Spring2017/slides/lecture_3-21-17.pdf 1203 2017 John Paisley ml Columbia
410 Maximum Margin Classifiers http://www.columbia.edu/~jwp2128/Teaching/W4721/Spring2017/slides/lecture_2-23-17.pdf 1061 2017 John Paisley ml Columbia
411 Bias-Variance Tradeoff http://www.columbia.edu/~jwp2128/Teaching/W4721/Spring2017/slides/lecture_1-26-17.pdf 1060 2017 John Paisley ml Columbia
412 Decision Trees http://www.columbia.edu/~jwp2128/Teaching/W4721/Spring2017/slides/lecture_2-28-17.pdf 1066 2017 John Paisley ml Columbia
413 Logistic Regression http://www.columbia.edu/~jwp2128/Teaching/W4721/Spring2017/slides/lecture_2-16-17.pdf 1058 2017 John Paisley ml Columbia
414 Machine Learning for Data Science http://www.columbia.edu/~jwp2128/Teaching/W4721/Spring2017/slides/lecture_1-17-17.pdf 1066 2017 John Paisley ml Columbia
415 Boosting http://www.columbia.edu/~jwp2128/Teaching/W4721/Spring2017/slides/lecture_3-2-17.pdf 1055 2017 John Paisley ml Columbia
416 Feature Expansions http://www.columbia.edu/~jwp2128/Teaching/W4721/Spring2017/slides/lecture_2-21-17.pdf 1163 2017 John Paisley ml Columbia
417 Linear Classification http://www.columbia.edu/~jwp2128/Teaching/W4721/Spring2017/slides/lecture_2-14-17.pdf 1153 2017 John Paisley ml Columbia
418 Hidden Markov Models http://www.columbia.edu/~jwp2128/Teaching/W4721/Spring2017/slides/lecture_4-13-17.pdf 1016 2017 John Paisley ml Columbia
419 Least Squares Continued http://www.columbia.edu/~jwp2128/Teaching/W4721/Spring2017/slides/lecture_1-24-17.pdf 1158 2017 John Paisley ml Columbia
420 Model Selection http://www.columbia.edu/~jwp2128/Teaching/W4721/Spring2017/slides/lecture_4-25-17.pdf 1066 2017 John Paisley ml Columbia
421 Bayesian Linear Regression http://www.columbia.edu/~jwp2128/Teaching/W4721/Spring2017/slides/lecture_1-31-17.pdf 1058 2017 John Paisley ml Columbia
422 Markov Chains http://www.columbia.edu/~jwp2128/Teaching/W4721/Spring2017/slides/lecture_4-11-17.pdf 1016 2017 John Paisley ml Columbia
423 Markov Models http://www.columbia.edu/~jwp2128/Teaching/W4721/Spring2017/slides/lecture_4-18-17.pdf 1016 2017 John Paisley ml Columbia
424 Linear Regression http://www.columbia.edu/~jwp2128/Teaching/W4721/Spring2017/slides/lecture_1-19-17.pdf 1158 2017 John Paisley ml Columbia
425 Minimum L2 Regression http://www.columbia.edu/~jwp2128/Teaching/W4721/Spring2017/slides/lecture_2-2-17.pdf 1158 2017 John Paisley ml Columbia
426 Soft Clustering vs Hard Clustering http://www.columbia.edu/~jwp2128/Teaching/W4721/Spring2017/slides/lecture_3-28-17.pdf 1041 2017 John Paisley ml Columbia
427 Association Analysis http://www.columbia.edu/~jwp2128/Teaching/W4721/Spring2017/slides/lecture_4-20-17.pdf 1202 2017 John Paisley ml Columbia
428 Topic Modeling http://www.columbia.edu/~jwp2128/Teaching/W4721/Spring2017/slides/lecture_4-4-17.pdf 1179 2017 John Paisley ml Columbia
429 Maximum Likelihood http://www.columbia.edu/~jwp2128/Teaching/W4721/Spring2017/slides/lecture_3-23-17.pdf 1555 2017 John Paisley ml Columbia
430 Collaborative Filtering http://www.columbia.edu/~jwp2128/Teaching/W4721/Spring2017/slides/lecture_3-30-17.pdf 1066 2017 John Paisley ml Columbia
431 Classification http://www.columbia.edu/~jwp2128/Teaching/W4721/Spring2017/slides/lecture_2-7-17.pdf 1153 2017 John Paisley ml Columbia
432 Introduction to Machine Learning http://ace.cs.ohio.edu/~razvan/courses/ml4900/lecture01.pdf 1066 2017 Razvan C. Bunescu ml Ohio University
433 Optimization http://ace.cs.ohio.edu/~razvan/courses/ml4900/lecture03.pdf 1320 2017 Razvan C. Bunescu ml Ohio University
434 k-Nearest Neighbors http://ace.cs.ohio.edu/~razvan/courses/ml4900/lecture08.pdf 1153 2017 Razvan C. Bunescu ml Ohio University
435 Fishers Linear Discriminant http://ace.cs.ohio.edu/~razvan/courses/ml4900/lecture05.pdf 1555 2017 Razvan C. Bunescu ml Ohio University
436 The Perceptron http://ace.cs.ohio.edu/~razvan/courses/ml4900/lecture06.pdf 1157 2017 Razvan C. Bunescu ml Ohio University
437 Canonical Correlation Analysis http://ace.cs.ohio.edu/~razvan/courses/ml4900/lecture07.pdf 1317 2017 Razvan C. Bunescu ml Ohio University
438 Machine Learning: Logistic Regression http://ace.cs.ohio.edu/~razvan/courses/ml4900/lecture04.pdf 1158 2017 Razvan C. Bunescu ml Ohio University
439 Supervised Learning http://ace.cs.ohio.edu/~razvan/courses/ml4900/lecture02.pdf 1066 2017 Razvan C. Bunescu ml Ohio University
440 Introduction https://www.cmpe.boun.edu.tr/~ethem/i2ml3e/3e_v1-0/i2ml3e-chap1.pdf 1066 2014 Ethem Alpaydın ml Boğaziçi University
441 Non-parametric Methods https://www.cmpe.boun.edu.tr/~ethem/i2ml3e/3e_v1-0/i2ml3e-chap8.pdf 1202 2014 Ethem Alpaydın ml Boğaziçi University
442 Dimensionality Reduction https://www.cmpe.boun.edu.tr/~ethem/i2ml3e/3e_v1-0/i2ml3e-chap6.pdf 1116 2014 Ethem Alpaydın ml Boğaziçi University
443 Clustering https://www.cmpe.boun.edu.tr/~ethem/i2ml3e/3e_v1-0/i2ml3e-chap7.pdf 1041 2014 Ethem Alpaydın ml Boğaziçi University
444 Supervised Learning https://www.cmpe.boun.edu.tr/~ethem/i2ml3e/3e_v1-0/i2ml3e-chap2.pdf 1004 2014 Ethem Alpaydın ml Boğaziçi University
445 Kernel Methods https://www.cmpe.boun.edu.tr/~ethem/i2ml3e/3e_v1-0/i2ml3e-chap13.pdf 1161 2014 Ethem Alpaydın ml Boğaziçi University
446 Bayesian Decision Theory https://www.cmpe.boun.edu.tr/~ethem/i2ml3e/3e_v1-0/i2ml3e-chap3.pdf 1330 2014 Ethem Alpaydın ml Boğaziçi University
447 Multivariate Methods https://www.cmpe.boun.edu.tr/~ethem/i2ml3e/3e_v1-0/i2ml3e-chap5.pdf 1202 2014 Ethem Alpaydın ml Boğaziçi University
448 Decision Trees https://www.cmpe.boun.edu.tr/~ethem/i2ml3e/3e_v1-0/i2ml3e-chap9.pdf 1066 2014 Ethem Alpaydın ml Boğaziçi University
449 Linear Discrimination https://www.cmpe.boun.edu.tr/~ethem/i2ml3e/3e_v1-0/i2ml3e-chap10.pdf 1155 2014 Ethem Alpaydın ml Boğaziçi University
450 Parametric Models https://www.cmpe.boun.edu.tr/~ethem/i2ml3e/3e_v1-0/i2ml3e-chap4.pdf 1202 2014 Ethem Alpaydın ml Boğaziçi University
451 Hidden Markov Models https://www.cmpe.boun.edu.tr/~ethem/i2ml3e/3e_v1-0/i2ml3e-chap15.pdf 1016 2014 Ethem Alpaydın ml Boğaziçi University
452 Multi-layer perceptrons https://www.cmpe.boun.edu.tr/~ethem/i2ml3e/3e_v1-0/i2ml3e-chap11.pdf 1157 2014 Ethem Alpaydın ml Boğaziçi University
453 Graphical Models https://www.cmpe.boun.edu.tr/~ethem/i2ml3e/3e_v1-0/i2ml3e-chap14.pdf 1315 2014 Ethem Alpaydın ml Boğaziçi University
454 Combining Multiple Learners https://www.cmpe.boun.edu.tr/~ethem/i2ml3e/3e_v1-0/i2ml3e-chap17.pdf 1066 2014 Ethem Alpaydın ml Boğaziçi University
455 Bayesian Estimation https://www.cmpe.boun.edu.tr/~ethem/i2ml3e/3e_v1-0/i2ml3e-chap16.pdf 1066 2014 Ethem Alpaydın ml Boğaziçi University
456 Local Models https://www.cmpe.boun.edu.tr/~ethem/i2ml3e/3e_v1-0/i2ml3e-chap12.pdf 1066 2014 Ethem Alpaydın ml Boğaziçi University
457 Reinforcement Learning https://www.cmpe.boun.edu.tr/~ethem/i2ml3e/3e_v1-0/i2ml3e-chap18.pdf 1336 2014 Ethem Alpaydın ml Boğaziçi University
458 Design and Analysis of Machine Learning Experiments https://www.cmpe.boun.edu.tr/~ethem/i2ml3e/3e_v1-0/i2ml3e-chap19.pdf 1066 2014 Ethem Alpaydın ml Boğaziçi University
459 Sequential Monte Carlo (Continued) and Conditional Linear Gaussian Models https://www.dropbox.com/s/z5c5vlfut4mw8ia/Lec21-ConditionalLinearGaussianModels.pdf?dl=0 1317 2017 Nicholas Zabaras ml Notre Dame
460 Importance Sampling Prof. Nicholas Zabaras https://www.dropbox.com/s/jv0r65uxy1gty9v/Lec15-ImportanceSampling.pdf?dl=0 1317 2017 Nicholas Zabaras ml Notre Dame
461 Robbins-Monro for Sequential MLE, Curse of Dimensionality, Conditional and Marginal Gaussian Distributions https://www.dropbox.com/s/j54clc30s2nthm5/Lec4-RobbinsMonro-CurseOfDimensionality-ConditionalGaussians.pdf?dl=0 1317 2017 Nicholas Zabaras ml Notre Dame
462 Implementation of Bayesian Regression and Variable Selection https://www.dropbox.com/s/pk2caw144nvzjcd/Lec12-CaterpillarRegressionExample.pdf?dl=0 1317 2017 Nicholas Zabaras ml Notre Dame
463 Exponential Family and Generalized Linear Models https://www.dropbox.com/s/q31dbbt01wqa071/Lec7-ExponentialFamilyGLMBayesianInf4MultivariateGaussian.pdf?dl=0 1317 2017 Nicholas Zabaras ml Notre Dame
464 Mixture Models and Expectation-Maximization https://www.dropbox.com/s/im3aw07lwe3eo5h/Lec24-MoreOnExpectationMaximization.pdf?dl=0’ 1317 2017 Nicholas Zabaras ml Notre Dame
465 Introduction to Probability and Statistics https://www.dropbox.com/s/f10vs0qrkh7428a/Lec2-IntroToProbabilityAndStatistics.pdf?dl=0 1555 2017 Nicholas Zabaras ml Notre Dame
466 Prior and Hierarchical Modeling https://www.dropbox.com/s/y6m9f11sshugfnw/Lec8-PriorHierarchicalModels.pdf?dl=0 1317 2017 Nicholas Zabaras ml Notre Dame
467 Introduction to Bayesian Linear Regression, Model Comparison and Selection https://www.dropbox.com/s/ecp5tkeefd7h47h/Lec9-Intro2RegressionOccamsRazor.pdf?dl=0 1158 2017 Nicholas Zabaras ml Notre Dame
468 Kernel Methods and Introduction to Gaussian Processes https://www.dropbox.com/s/2yonhmzn57lvv1d/Lec27-KernelMethods.pdf?dl=0 1158 2017 Nicholas Zabaras ml Notre Dame
469 Gaussian Processes for Classification Problems, Course Summary https://www.dropbox.com/s/54pudgwif0zo8mt/Lec28-Summary.pdf?dl=0 1161 2017 Nicholas Zabaras ml Notre Dame
470 Statistical Computing for Scientists and Engineers https://www.dropbox.com/s/121sglcweantr2d/Lec1-IntroToProbabilityAndStatistics.pdf?dl=0" 1317 2017 Nicholas Zabaras ml Notre Dame
471 Variable Dimension Models: Reversible Jump MCMC https://www.dropbox.com/s/i7d28g05ikfpt79/Lec22-ReversibleJumpMCMC.pdf?dl=0 1317 2017 Nicholas Zabaras ml Notre Dame
472 Continuous Latent Variable Models: Probabilistic and Bayesian PCA,Kernel PCA https://www.dropbox.com/s/xhe95z6yoic8207/Lec26-ContinuousLatentVariableModels.pdf?dl=0 1317 2017 Nicholas Zabaras ml Notre Dame
473 Sequential Monte Carlo (Continued) and Conditional Linear Gaussian Models https://www.dropbox.com/s/z5c5vlfut4mw8ia/Lec21-ConditionalLinearGaussianModels.pdf?dl=0 1317 2017 Nicholas Zabaras ml Notre Dame
474 Introduction to Bayesian Statistics, Exponential Family of Distributions https://www.dropbox.com/s/ggwvlumy240o6fk/Lec6-Intro2BayesianStatisticsAndTheExponentialFamily.pdf?dl=0 1317 2017 Nicholas Zabaras ml Notre Dame
475 Gibbs Sampling https://www.dropbox.com/s/g2s1g9u9vj6779o/Lec16-GibbsSampling.pdf?dl=0 1317 2017 Nicholas Zabaras ml Notre Dame
476 Reverse Sampling, Transformation Methods, Composition Methods, Accept-Reject Methods, Stratified/Systematic Sampling https://www.dropbox.com/s/b18tdp81l9mx45z/Lec14-RandomVariableGeneration.pdf?dl=0 1317 2017 Nicholas Zabaras ml Notre Dame
477 Information Theory, Multivariate Gaussian, MLE Estimation, Robbins-Monro algorithm https://www.dropbox.com/s/q23vr7ok2nkc2gg/Lec3-InfoTheory-CLTApplications-MLE.pdf?dl=0 1317 2017 Nicholas Zabaras ml Notre Dame
478 Introduction to State Space Models and Sequential Importance Sampling https://www.dropbox.com/s/pb555x6z5i8kxd8/Lec18-Intro2StateSpaceModelsAndSequentialImportanceSampling.pdf?dl=0 1317 2017 Nicholas Zabaras ml Notre Dame
479 Introduction to Monte Carlo Methods, Sampling from Discrete and Continuum Distributions https://www.dropbox.com/s/a8wyucgomltz8sa/Lec13-MonteCarloMethods.pdf?dl=0 1317 2017 Nicholas Zabaras ml Notre Dame
480 Markov Chain Monte Carlo and Metropolis-Hasting Algorithm https://www.dropbox.com/s/ek4n2cxtr8pfv7x/Lec17-MetropolisHastings.pdf?dl=0 1317 2017 Nicholas Zabaras ml Notre Dame
481 Principal Component Analysis https://www.dropbox.com/s/xf20q0jagldxvnj/Lec25-Intro2PCA.pdf?dl=0 1116 2017 Nicholas Zabaras ml Notre Dame
482 Sequential Importance Sampling with Resampling https://www.dropbox.com/s/vl10u6yrj7yenny/Lec19-SequentialImportanceSamplingResampling.pdf?dl=0 1555 2017 Nicholas Zabaras ml Notre Dame
483 Bayesian Linear Regression (continued) https://www.dropbox.com/s/d6zruv69fmu5wmi/Lec11-RegressionModelsB.pdf?dl=0 1158 2017 Nicholas Zabaras ml Notre Dame
484 Bayesian Regression https://www.dropbox.com/s/zt6w0wwmg866xtx/Lec10-RegressionModelsA.pdf?dl=0 1158 2017 Nicholas Zabaras ml Notre Dame
485 Introduction to Expectation-Maximization (EM) https://www.dropbox.com/s/2xoalhj26sh4da2/Lec23-IntroductiontoExpectationMaximization.pdf?dl=0 1145 2017 Nicholas Zabaras ml Notre Dame
486 Likelihood calculations, MAP estimate and Regularized Least Squares, Linear Gaussian Models https://www.dropbox.com/s/5wethziitk5wzmk/Lec5-Likelihood-MAP-GaussianLinearSystems.pdf?dl=0 1555 2017 Nicholas Zabaras ml Notre Dame
487 Decision trees http://people.csail.mit.edu/dsontag/courses/ml16/slides/lecture11.pdf 1059 2016 David Sontag ml New York University
488 Unsupervised learning (part1) http://people.csail.mit.edu/dsontag/courses/ml16/slides/lecture19.pdf 1563 2016 David Sontag ml New York University
489 Gradient, Subgradient and how they may affect your grade(ient) http://people.csail.mit.edu/dsontag/courses/ml16/slides/notes_convexity16.pdf 1320 2016 David Sontag ml New York University
490 Hierarchical & Spectral clustering http://people.csail.mit.edu/dsontag/courses/ml16/slides/lecture13.pdf 1041 2016 David Sontag ml New York University
491 Introduction to Bayesian methods continued 2 http://people.csail.mit.edu/dsontag/courses/ml16/slides/lecture17.pdf 1330 2016 David Sontag ml New York University
492 Learning theory and Decision trees http://people.csail.mit.edu/dsontag/courses/ml16/slides/lecture10.pdf 1568 2016 David Sontag ml New York University
493 Introduction To Machine Learning http://people.csail.mit.edu/dsontag/courses/ml16/slides/lecture21.pdf 1066 2016 David Sontag ml New York University
494 Clustering http://people.csail.mit.edu/dsontag/courses/ml16/slides/lecture12.pdf 1041 2016 David Sontag ml New York University
495 Introduction to Bayesian methods http://people.csail.mit.edu/dsontag/courses/ml16/slides/lecture14.pdf 1330 2016 David Sontag ml New York University
496 Introduction to Bayesian methods continued 1 http://people.csail.mit.edu/dsontag/courses/ml16/slides/lecture16.pdf 1330 2016 David Sontag ml New York University
497 Introduction to Learning http://people.csail.mit.edu/dsontag/courses/ml16/slides/lecture2.pdf 1568 2016 David Sontag ml New York University
498 Learning theory http://people.csail.mit.edu/dsontag/courses/ml16/slides/lecture9.pdf 1568 2016 David Sontag ml New York University
499 Support vector machines (SVMs) Lecture 3 http://people.csail.mit.edu/dsontag/courses/ml16/slides/lecture3.pdf 1038 2016 David Sontag ml New York University
500 Bayesian networks http://people.csail.mit.edu/dsontag/courses/ml16/slides/lecture18.pdf 1330 2016 David Sontag ml New York University
501 Support vector machines (SVMs) http://people.csail.mit.edu/dsontag/courses/ml16/slides/lecture5.pdf 1038 2016 David Sontag ml New York University
502 Introduction To Machine Learning http://people.csail.mit.edu/dsontag/courses/ml16/slides/lecture22.pdf 1559 2016 David Sontag ml New York University
503 L1 regularization & Intro to learning theory http://people.csail.mit.edu/dsontag/courses/ml16/slides/lecture8.pdf 1559 2016 David Sontag ml New York University
504 Neural networks http://people.csail.mit.edu/dsontag/courses/ml16/slides/lecture24.pdf 1181 2016 David Sontag ml New York University
505 Support vector machines (SVMs) http://people.csail.mit.edu/dsontag/courses/ml16/slides/lecture6.pdf 1161 2016 David Sontag ml New York University
506 Introduction to Machine Learning http://people.csail.mit.edu/dsontag/courses/ml16/slides/lecture1.pdf 1066 2016 David Sontag ml New York University
507 A History of AI http://people.csail.mit.edu/dsontag/courses/ml16/slides/lecture20.pdf 1007 2016 David Sontag ml New York University
508 Machine Learning http://people.csail.mit.edu/dsontag/courses/ml16/slides/lecture6_notes.pdf 1066 2016 David Sontag ml New York University
509 Dimensionality Reduction http://people.csail.mit.edu/dsontag/courses/ml16/slides/lecture23.pdf 1026 2016 David Sontag ml New York University
510 Support vector machines (SVMs) http://people.csail.mit.edu/dsontag/courses/ml16/slides/lecture4.pdf 1038 2016 David Sontag ml New York University
511 Support Vector Machines:Introduction https://courseworks.columbia.edu/access/content/group/COMSW4721_001_2014_1/lecture11.pdf 1038 2014 Ansaf Salleb-Aouissi ml Columbia University
512 Maximum A Posteriori and Expectation Maximization https://courseworks.columbia.edu/access/content/group/COMSW4721_001_2014_1/lecture19.pdf 1314 2014 Ansaf Salleb-Aouissi ml Columbia University
513 EM for GMM https://courseworks.columbia.edu/access/content/group/COMSW4721_001_2014_1/lecture21_complement_EM.pdf 1314 2014 Ansaf Salleb-Aouissi ml Columbia University
514 Support Vector Machines: Kernels https://courseworks.columbia.edu/access/content/group/COMSW4721_001_2014_1/lecture13.pdf 1038 2014 Ansaf Salleb-Aouissi ml Columbia University
515 Neural Networks https://courseworks.columbia.edu/access/content/group/COMSW4721_001_2014_1/lecture9_10.pdf 1181 2014 Ansaf Salleb-Aouissi ml Columbia University
516 Linear classification: Logistic Regression https://courseworks.columbia.edu/access/content/group/COMSW4721_001_2014_1/lecture7.pdf 1158 2014 Ansaf Salleb-Aouissi ml Columbia University
517 Mining Frequent Patterns and Association Rules https://courseworks.columbia.edu/access/content/group/COMSW4721_001_2014_1/lecture17.pdf 1559 2014 Ansaf Salleb-Aouissi ml Columbia University
518 Bootstrapping and Face Detection Application https://courseworks.columbia.edu/access/content/group/COMSW4721_001_2014_1/lecture15.pdf 1066 2014 Ansaf Salleb-Aouissi ml Columbia University
519 K-Means, GMMs and EM https://courseworks.columbia.edu/access/content/group/COMSW4721_001_2014_1/lecture21.pdf 1041 2014 Ansaf Salleb-Aouissi ml Columbia University
520 Support Vector Machines: primal, dual forms and soft-margin https://courseworks.columbia.edu/access/content/group/COMSW4721_001_2014_1/lecture12.pdf 1161 2014 Ansaf Salleb-Aouissi ml Columbia University
521 Ensemble Methods:Boosting, Bagging and Random Forests https://courseworks.columbia.edu/access/content/group/COMSW4721_001_2014_1/lecture14.pdf 1066 2014 Ansaf Salleb-Aouissi ml Columbia University
522 Maximum Likelihood and Gaussian Models https://courseworks.columbia.edu/access/content/group/COMSW4721_001_2014_1/lecture16.pdf 1165 2014 Ansaf Salleb-Aouissi ml Columbia University
523 Basic Concepts K-nearest neighbors https://courseworks.columbia.edu/access/content/group/COMSW4721_001_2014_1/lecture2.pdf 1066 2014 Ansaf Salleb-Aouissi ml Columbia University
524 Basic Concepts II https://courseworks.columbia.edu/access/content/group/COMSW4721_001_2014_1/lecture3.pdf 1066 2014 Ansaf Salleb-Aouissi ml Columbia University
525 Naive Bayes Classifier https://courseworks.columbia.edu/access/content/group/COMSW4721_001_2014_1/lecture18.pdf 1004 2014 Ansaf Salleb-Aouissi ml Columbia University
526 Linear Regression, Ridge regression, and Lasso https://courseworks.columbia.edu/access/content/group/COMSW4721_001_2014_1/lecture5.pdf 1004 2014 Ansaf Salleb-Aouissi ml Columbia University
527 Tree Classifiers (Decision Trees) https://courseworks.columbia.edu/access/content/group/COMSW4721_001_2014_1/lecture8.pdf 1004 2014 Ansaf Salleb-Aouissi ml Columbia University
528 Linear Classification: Perceptron https://courseworks.columbia.edu/access/content/group/COMSW4721_001_2014_1/lecture6.pdf 1004 2014 Ansaf Salleb-Aouissi ml Columbia University
529 Mining Frequent Patterns and Association Rules (continued) https://courseworks.columbia.edu/access/content/group/COMSW4721_001_2014_1/lecture17_complement.pdf 1559 2014 Ansaf Salleb-Aouissi ml Columbia University
530 Introduction https://courseworks.columbia.edu/access/content/group/COMSW4721_001_2014_1/lecture1.pdf 1559 2014 Ansaf Salleb-Aouissi ml Columbia University
531 Naive Bayes: Text Classification https://courseworks.columbia.edu/access/content/group/COMSW4721_001_2014_1/lecture20.pdf 1155 2014 Ansaf Salleb-Aouissi ml Columbia University
532 Linear Regression, Least Squares and Gradient Descent https://courseworks.columbia.edu/access/content/group/COMSW4721_001_2014_1/lecture4.pdf 1004 2014 Ansaf Salleb-Aouissi ml Columbia University
533 Neural Style http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/slides/lec13c.pdf 1049 2018 Roger Grosse ml University of Toronto
534 Backpropagation http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/slides/lec6.pdf 1182 2018 Roger Grosse ml University of Toronto
535 Introduction http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/slides/lec1.pdf 1066 2018 Roger Grosse ml University of Toronto
536 Multilayer Perceptrons http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/slides/lec5.pdf 1157 2018 Roger Grosse ml University of Toronto
537 Optimization http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/slides/lec7.pdf 1320 2018 Roger Grosse ml University of Toronto
538 Recurrent Neural Networks http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/slides/lec14.pdf 1190 2018 Roger Grosse ml University of Toronto
539 ResNets and Attention http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/slides/lec16.pdf 1194 2018 Roger Grosse ml University of Toronto
540 Distributed Representations http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/slides/lec10.pdf 1186 2018 Roger Grosse ml University of Toronto
541 Mixture Modeling http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/slides/lec18.pdf 1165 2018 Roger Grosse ml University of Toronto
542 Boltzmann Machines http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/slides/lec19.pdf 1183 2018 Roger Grosse ml University of Toronto
543 Go http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/slides/lec23.pdf 1083 2018 Roger Grosse ml University of Toronto
544 Intro to Machine Learning and Neural Networks http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/slides/lec13b.pdf 1084 2018 Roger Grosse ml University of Toronto
545 Convolutional Networks http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/slides/lec11.pdf 1193 2018 Roger Grosse ml University of Toronto
546 Learning Probabilistic Models http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/slides/lec17.pdf 1202 2018 Roger Grosse ml University of Toronto
547 Python http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/slides/lec8a.pdf 1063 2018 Roger Grosse ml University of Toronto
548 Adversarial Learning http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/slides/lec22.pdf 1306 2018 Roger Grosse ml University of Toronto
549 Autoencoders http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/slides/lec20.pdf 1182 2018 Roger Grosse ml University of Toronto
550 Image Classification http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/slides/lec12.pdf 1182 2018 Roger Grosse ml University of Toronto
551 Vote http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/slides/lec3.pdf 9999 2018 Roger Grosse ml University of Toronto
552 Generalization http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/slides/lec9.pdf 9999 2018 Roger Grosse ml University of Toronto
553 Learning a Classier http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/slides/lec4.pdf 1004 2018 Roger Grosse ml University of Toronto
554 Exploding and Vanishing Gradients http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/slides/lec15.pdf 1182 2018 Roger Grosse ml University of Toronto
555 Neural Networks for Machine Learning http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/slides/lec20_hinton.pdf 1181 2018 Roger Grosse ml University of Toronto
556 Implementing autograd http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/slides/lec8b.pdf 1182 2018 Roger Grosse ml University of Toronto
557 Understanding How ConvNets Works http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/slides/lec13a.pdf 1193 2018 Roger Grosse ml University of Toronto
558 Bayesian Hyperparameter Optimization http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/slides/lec21.pdf 1320 2018 Roger Grosse ml University of Toronto
559 Linear Regression http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/slides/lec2.pdf 1004 2018 Roger Grosse ml University of Toronto
560 Optimization for ML http://www.cs.cmu.edu/~mgormley/courses/10601-s17/slides/lecture7-opt.pdf 1320 2016 Matt Gormley ml Carnegie Mellon University
561 Support vector machines (SVMs) http://www.cs.cmu.edu/~mgormley/courses/10601-s17/slides/lecture12-svm.pdf 1038 2016 Matt Gormley ml Carnegie Mellon University
562 Backpropagation http://www.cs.cmu.edu/~mgormley/courses/10601-s17/slides/lecture20-backprop.pdf 1181 2016 Matt Gormley ml Carnegie Mellon University
563 Learning Theory http://www.cs.cmu.edu/~mgormley/courses/10601-s17/slides/lecture13-theory.pdf 1568 2016 Matt Gormley ml Carnegie Mellon University
564 Gaussian Naive Bayes http://www.cs.cmu.edu/~mgormley/courses/10601-s17/slides/lecture6-gnb.pdf 1155 2016 Matt Gormley ml Carnegie Mellon University
565 The Probabilistic Approach to Learning from Data http://www.cs.cmu.edu/~mgormley/courses/10601-s17/slides/lecture4-prob.pdf 1059 2016 Matt Gormley ml Carnegie Mellon University
566 Machine Learning in Practice + k-Nearest Neighbors http://www.cs.cmu.edu/~mgormley/courses/10601-s17/slides/lecture2-knn.pdf 1168 2016 Matt Gormley ml Carnegie Mellon University
567 Perceptron and Kernels http://www.cs.cmu.edu/~mgormley/courses/10601-s17/slides/lecture11-kernels.pdf 1337 2016 Matt Gormley ml Carnegie Mellon University
568 Overview http://www.cs.cmu.edu/~mgormley/courses/10601-s17/slides/lecture1-overview.pdf 1580 2016 Matt Gormley ml Carnegie Mellon University
569 CNNs http://www.cs.cmu.edu/~mgormley/courses/10601-s17/slides/lecture21-cnn.pdf 1193 2016 Matt Gormley ml Carnegie Mellon University
570 Logistic Regression, Nonlinear Features, Regularization http://www.cs.cmu.edu/~mgormley/courses/10601-s17/slides/lecture9-logreg.pdf 1158 2016 Matt Gormley ml Carnegie Mellon University
571 K-Means and GMMs http://www.cs.cmu.edu/~mgormley/courses/10601-s17/slides/lecture16-gmm.pdf 1167 2016 Matt Gormley ml Carnegie Mellon University
572 Linear Regression http://www.cs.cmu.edu/~mgormley/courses/10601-s17/slides/lecture8-linreg.pdf 1057 2016 Matt Gormley ml Carnegie Mellon University
573 k-Nearest Neighbors http://www.cs.cmu.edu/~mgormley/courses/10601-s17/slides/lecture3-exp.pdf 1153 2016 Matt Gormley ml Carnegie Mellon University
574 Clustering(K-Means) http://www.cs.cmu.edu/~mgormley/courses/10601-s17/slides/lecture15-cluster.pdf 1167 2016 Matt Gormley ml Carnegie Mellon University
575 Midterm http://www.cs.cmu.edu/~mgormley/courses/10601-s17/slides/lecture14-midterm.pdf 1580 2016 Matt Gormley ml Carnegie Mellon University
576 PCA and Neural Networks http://www.cs.cmu.edu/~mgormley/courses/10601-s17/slides/lecture18-pca.pdf 1181 2016 Matt Gormley ml Carnegie Mellon University
577 Bayesian Networks http://www.cs.cmu.edu/~mgormley/courses/10601-s17/slides/lecture22-bayesnet1.pdf 1330 2016 Matt Gormley ml Carnegie Mellon University
578 Neural Networks and Backpropagation http://www.cs.cmu.edu/~mgormley/courses/10601-s17/slides/lecture19-nn.pdf 1182 2016 Matt Gormley ml Carnegie Mellon University
579 EM and PCA http://www.cs.cmu.edu/~mgormley/courses/10601-s17/slides/lecture17-em.pdf 1189 2016 Matt Gormley ml Carnegie Mellon University
580 MLE/MAP and Naïve Bayes http://www.cs.cmu.edu/~mgormley/courses/10601-s17/slides/lecture5-nb.pdf 1330 2016 Matt Gormley ml Carnegie Mellon University
581 Regularization and Perceptron http://www.cs.cmu.edu/~mgormley/courses/10601-s17/slides/lecture10-perc.pdf 1337 2016 Matt Gormley ml Carnegie Mellon University
582 Naive Bayes http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/03_MLE_MAP_NBayes-1-21-2015_ann.pdf 1004 2015 Tom M. Mitchell ml Carnegie Mellon University
583 Machine Learning and Differential Privacy http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/26_privacy_4-22-2015.pdf 9999 2015 Maria-Florina Balcan ml Carnegie Mellon University
584 Support Vector Machines (SVMs) http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/18_svm-ssl_03-25-2015.pdf 1038 2015 Maria-Florina Balcan ml Carnegie Mellon University
585 Reinforcement Learning http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/25_rl_4-20-2015.pdf 1336 2015 Maria-Florina Balcan ml Carnegie Mellon University
586 Sample Complexity for Function Approximation and Model Selection http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/10_Theory3_2-16-2015.pdf 9999 2015 Tom M. Mitchell ml Carnegie Mellon University
587 Bayesian Network http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/12_GrMod1_2-23-2015-ann.pdf 1330 2015 Tom M. Mitchell ml Carnegie Mellon University
588 PCA, Kernel PCA, ICA Learning Representations. Dimensionality Reduction. http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/22_pca-04-09-2015.pdf 1026 2015 Maria-Florina Balcan ml Carnegie Mellon University
589 Boosting Approach to ML Perceptron, Margins, Kernels http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/16_boosting-percepton-margins_03-18-2015.pdf 1569 2015 Maria-Florina Balcan ml Carnegie Mellon University
590 Graphical models and Bayes Nets http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/13_GrMod3_2-25-2015-ann.pdf 1330 2015 Tom M. Mitchell ml Carnegie Mellon University
591 Bayes Rule and Estimating parameters http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/03_MLE_MAP_NBayes-1-21-2015.pdf 1060 2015 Tom M. Mitchell ml Carnegie Mellon University
592 The Boosting Approach to Machine Learning http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/15_boosting_3-16-2015.pdf 1066 2015 Maria-Florina Balcan ml Carnegie Mellon University
593 Bias, Variance and Error http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/11_GrMod1_2-18-2015-ann.pdf 1066 2015 Tom M. Mitchell ml Carnegie Mellon University
594 Semi-Supervised Learning http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/19_ssl_03-30-2015.pdf 1203 2015 Maria-Florina Balcan ml Carnegie Mellon University
595 Math Review and Decision Trees http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/r01_math_decision_trees.pdf 1153 2015 Maria-Florina Balcan ml Carnegie Mellon University
596 Graphical models and Bayes Nets http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/11_GrMod1_2-18-2015.pdf 1330 2015 Tom M. Mitchell ml Carnegie Mellon University
597 Machine Learning Theory II http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/09_Theory2_2-11-2015.pdf 1066 2015 Tom M. Mitchell ml Carnegie Mellon University
598 Naive Bayes http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/05_GNB_1-28-2015.pdf 1330 2015 Tom M. Mitchell ml Carnegie Mellon University
599 Overfitting and probability http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/02_Overfitting_ProbReview-1-14-2015_ann.pdf 1058 2015 Tom M. Mitchell ml Carnegie Mellon University
600 Never-Ending Language Learning http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/23_nell_4-13-2015.pdf 1012 2015 Maria-Florina Balcan ml Carnegie Mellon University
601 Naive Bayes http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/04_NBayes-1-26-2015.pptx.pdf 1004 2015 Tom M. Mitchell ml Carnegie Mellon University
602 Overfitting and probability http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/02_Overfitting_ProbReview-1-14-2015.pdf 1058 2015 Tom M. Mitchell ml Carnegie Mellon University
603 Clustering. Unsupervised Learning http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/21_clustering_4-6-2015.pdf 1041 2015 Maria-Florina Balcan ml Carnegie Mellon University
604 Logistic regression and Generative/Discriminative classifiers http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/06_GenDiscr_LR_2-2-2015.pdf 1155 2015 Tom M. Mitchell ml Carnegie Mellon University
605 Bayes Classifiers http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/04_NBayes-1-26-2015-ann.pdf 1330 2015 Tom M. Mitchell ml Carnegie Mellon University
606 Linear regression http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/07_GenDiscr2_2-4-2015-ann.pdf 1153 2015 Tom M. Mitchell ml Carnegie Mellon University
607 Graphical models and Bayes Nets http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/14_GrMod4_3-4-2015-ann.pdf 1330 2015 Tom M. Mitchell ml Carnegie Mellon University
608 Graphical models and Bayes Nets http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/14_GrMod4_3-4-2015.pdf 1330 2015 Tom M. Mitchell ml Carnegie Mellon University
609 Machine Learning Theory http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/08_Theory_2-9-2015.pdf 1066 2015 Tom M. Mitchell ml Carnegie Mellon University
610 Kernels Methods in Machine Learning http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/17_margins-kernels_03-23-2015.pdf 1157 2015 Maria-Florina Balcan ml Carnegie Mellon University
611 Active Learning http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/20_al_4-1-2015.pdf 9999 2015 Maria-Florina Balcan ml Carnegie Mellon University
612 Impact of Deep Learning http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/26_deep_4-22-2015.pdf 1049 2015 Maria-Florina Balcan ml Carnegie Mellon University
613 Naive Bayes http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/05_GNB_1-28-2015-ann.pdf 1330 2015 Tom M. Mitchell ml Carnegie Mellon University
614 Graphical models and Bayes Nets http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/13_GrMod3_2-25-2015.pdf 1330 2015 Tom M. Mitchell ml Carnegie Mellon University
615 10-601 Machine Learning Maria-Florina Balcan http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/perceptron-notes.pdf 1157 2015 Maria-Florina Balcan ml Carnegie Mellon University
616 Decision Trees http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/01_DTreesAndOverfitting-1-12-2015.pdf 1004 2015 Tom M. Mitchell ml Carnegie Mellon University
617 Linear regression http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/07_GenDiscr2_2-4-2015.pdf 1153 2015 Tom M. Mitchell ml Carnegie Mellon University
618 Logistic regression and Generative/Discriminative classifiers http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/06_GenDiscr_LR_2-2-2015-ann.pdf 1155 2015 Tom M. Mitchell ml Carnegie Mellon University
619 Neural Networks, Backpropagation, Recurrent Networks, Convolutional Networks, Deep Belief Networks, Deep Boltzman Machines http://www.cs.cmu.edu/~ninamf/courses/601sp15/slides/24_nn_4-15-2015.pdf 1181 2015 Maria-Florina Balcan ml Carnegie Mellon University
620 EM Algorithm http://www.seas.upenn.edu/~cis519/spring2018/assets/lectures/lecture-10/10-LecEM.pdf 1189 2018 Dan Roth ml University of Pennsylvania
621 Lecture Introduction http://www.seas.upenn.edu/~cis519/spring2018/assets/lectures/lecture-1/01-LecIntro.pdf 1580 2018 Dan Roth ml University of Pennsylvania
622 Evaluation http://www.seas.upenn.edu/~cis519/spring2018/assets/lectures/lecture-3/03-eval.pdf 1143 2018 Dan Roth ml University of Pennsylvania
623 Lecture Introduction http://www.seas.upenn.edu/~cis519/spring2018/assets/lectures/lecture-0/00-LecIntro.pdf 1580 2018 Dan Roth ml University of Pennsylvania
624 Neural Networks http://www.seas.upenn.edu/~cis519/spring2018/assets/lectures/lecture-8/08-LecNN.pdf 1181 2018 Dan Roth ml University of Pennsylvania
625 Probability and Bayes Theorem http://www.seas.upenn.edu/~cis519/spring2018/assets/lectures/lecture-9/09-LecBayes.pdf 1059 2018 Dan Roth ml University of Pennsylvania
626 Decision Trees http://www.seas.upenn.edu/~cis519/spring2018/assets/lectures/lecture-2/02-LecDT.pdf 1004 2018 Dan Roth ml University of Pennsylvania
627 Perceptron http://www.seas.upenn.edu/~cis519/spring2018/assets/lectures/lecture-4/04-LecOnLine.pdf 1157 2018 Dan Roth ml University of Pennsylvania
628 Support Vector Machines http://www.seas.upenn.edu/~cis519/spring2018/assets/lectures/lecture-6/06-LecSVM.pdf 1038 2018 Dan Roth ml University of Pennsylvania
629 RNN and Memory https://github.com/joanbruna/stat212b/blob/master/lec13.pdf 1190 2016 Joan Bruna ml GitHub
630 Classification with Scattering https://github.com/joanbruna/stat212b/blob/master/lec6.pdf 1037 2016 Joan Bruna ml GitHub
631 Introduction https://github.com/joanbruna/stat212b/blob/master/lec1.pdf 1580 2016 Joan Bruna ml GitHub
632 Seperable Scattering Operators https://github.com/joanbruna/stat212b/blob/master/lec5.pdf 1004 2016 Joan Bruna ml GitHub
633 CNN and near-diagonalisation https://github.com/joanbruna/stat212b/blob/master/lec7.pdf 1193 2016 Joan Bruna ml GitHub
634 Sequence Structured Prediction https://github.com/joanbruna/stat212b/blob/master/lec14.pdf 1086 2016 Joan Bruna ml GitHub
635 Latent Graphical Models https://github.com/joanbruna/stat212b/blob/master/lec16.pdf 1321 2016 Joan Bruna ml GitHub
636 Embeddings, Extensions to Non-Euclidean Domains, Representations of Stationary Processes https://github.com/joanbruna/stat212b/blob/master/lec10.pdf 1049 2016 Joan Bruna ml GitHub
637 Gibbs models https://github.com/joanbruna/stat212b/blob/master/lec18.pdf 1087 2016 Joan Bruna ml GitHub
638 GAN, MCMC, Self-Supervised Learning https://github.com/joanbruna/stat212b/blob/master/lec17.pdf 1066 2016 Joan Bruna ml GitHub
639 Discrete Neural Turing Machines https://github.com/joanbruna/stat212b/blob/master/lec11_guest_wojciechzaremba.pdf 1066 2016 Joan Bruna ml GitHub
640 Representation of Stationary Process https://github.com/joanbruna/stat212b/blob/master/lec12.pdf 1181 2016 Joan Bruna ml GitHub
641 Transformation Groups https://github.com/joanbruna/stat212b/blob/master/lec3.pdf 1163 2016 Joan Bruna ml GitHub
642 Random Forests https://github.com/joanbruna/stat212b/blob/master/lec9.pdf 1004 2016 Joan Bruna ml GitHub
643 Local invariants and convolution https://github.com/joanbruna/stat212b/blob/master/lec4.pdf 1163 2016 Joan Bruna ml GitHub
644 Auto encoders https://github.com/joanbruna/stat212b/blob/master/lec15.pdf 1070 2016 Joan Bruna ml GitHub
645 Selection Models https://github.com/joanbruna/stat212b/blob/master/lec8.pdf 1536 2016 Joan Bruna ml GitHub
646 Classification, kernels and metrics https://github.com/joanbruna/stat212b/blob/master/lec2.pdf 1153 2016 Joan Bruna ml GitHub
647 Information Retrieval 3 https://course.ccs.neu.edu/cs6200f13/cs6200-f13-3.pdf 1042 2013 David Smith ir Northeastern University
648 Information Retrieval 1 https://course.ccs.neu.edu/cs6200f13/cs6200-f13-1.pdf 1042 2013 David Smith ir Northeastern University
649 Information Retrieval 8 https://course.ccs.neu.edu/cs6200f13/cs6200-f13-8.pdf 1042 2013 David Smith ir Northeastern University
650 Information Retrieval 4 https://course.ccs.neu.edu/cs6200f13/cs6200-f13-4.pdf 1042 2013 David Smith ir Northeastern University
651 Information Retrieval 5 https://course.ccs.neu.edu/cs6200f13/cs6200-f13-5.pdf 1042 2013 David Smith ir Northeastern University
652 Information Retrieval 7 https://course.ccs.neu.edu/cs6200f13/cs6200-f13-7.pdf 1042 2013 David Smith ir Northeastern University
653 Information Retrieval 6 https://course.ccs.neu.edu/cs6200f13/cs6200-f13-6.pdf 1042 2013 David Smith ir Northeastern University
654 Information Retrieval 2 https://course.ccs.neu.edu/cs6200f13/cs6200-f13-2.pdf 1042 2013 David Smith ir Northeastern University
655 Experiences with MapReduce, an Abstraction for Large-Scale Computation http://web.stanford.edu/class/cs276/handouts/mapreduce-pact06-keynote.pdf 1303 2017 Christopher Manning ir Stanford University
656 Hypertext and Link Analysis http://web.stanford.edu/class/cs276/handouts/lecture17-linkanalysis-six-per-page.pdf 1005 2017 Christopher Manning ir Stanford University
657 Efficient Scoring http://web.stanford.edu/class/cs276/handouts/lecture10-efficient-scoring-six-per-page.pdf 1005 2017 Christopher Manning ir Stanford University
658 Introducing Information Retrieval and Web Search http://web.stanford.edu/class/cs276/handouts/lecture1-intro-handout-1-per.pdf 1169 2017 Christopher Manning ir Stanford University
659 Distributed representations http://web.stanford.edu/class/cs276/handouts/lecture20-distributed-representations-6up.pdf 1186 2017 Christopher Manning ir Stanford University
660 Tolerant Retrieval http://web.stanford.edu/class/cs276/handouts/lecture3-tolerant-retrieval-handout-6-per.pdf 1042 2017 Christopher Manning ir Stanford University
661 Text Classification 1 http://web.stanford.edu/class/cs276/handouts/lecture12-textcat.pdf 1039 2017 Christopher Manning ir Stanford University
662 Ranking Principle http://web.stanford.edu/class/cs276/handouts/lecture11-probir-6up.pdf 1042 2017 Christopher Manning ir Stanford University
663 Compression http://web.stanford.edu/class/cs276/handouts/lecture5-compression-handout-6-per.pdf 1042 2017 Christopher Manning ir Stanford University
664 Hypertext and Link Analysis http://web.stanford.edu/class/cs276/handouts/lecture17-linkanalysis-one-per-page.pdf 1005 2017 Christopher Manning ir Stanford University
665 Evaluation http://web.stanford.edu/class/cs276/handouts/lecture8-evaluation_2014-one-per-page.pdf 1042 2017 Christopher Manning ir Stanford University
666 Document Ingestion,Tokenization, Stemming and Lemmatization http://web.stanford.edu/class/cs276/handouts/lecture2-dictionary-handout-1-per.pdf 1042 2017 Christopher Manning ir Stanford University
667 Evaluation http://web.stanford.edu/class/cs276/handouts/lecture8-evaluation-handout-1-per.pdf 1042 2017 Christopher Manning ir Stanford University
668 Efficient Scoring http://web.stanford.edu/class/cs276/handouts/lecture10-efficient-scoring-one-per-page.pdf 1005 2017 Christopher Manning ir Stanford University
669 Text Classification 1 http://web.stanford.edu/class/cs276/handouts/lecture12-textcat-6up.pdf 1039 2017 Christopher Manning ir Stanford University
670 Information Retrieval: Lecture Introduction http://web.stanford.edu/class/cs276/handouts/lecture1-intro-handout-6-per.pdf 1580 2017 Christopher Manning ir Stanford University
671 Index Construction http://web.stanford.edu/class/cs276/handouts/lecture4-indexconstruction-handout-6-per.pdf 1042 2017 Christopher Manning ir Stanford University
672 TF-IDF http://web.stanford.edu/class/cs276/handouts/lecture6-tfidf-handout-1-per.pdf 1559 2017 Christopher Manning ir Stanford University
673 Web Crawling http://web.stanford.edu/class/cs276/handouts/lecture18-crawling-6-per-page.pdf 1559 2017 Christopher Manning ir Stanford University
674 Personalization http://web.stanford.edu/class/cs276/handouts/personalization-lecture-1-per-page.pdf 1559 2017 Christopher Manning ir Stanford University
675 SVMs http://web.stanford.edu/class/cs276/handouts/lecture13-SVMs-6up.pdf 1038 2017 Christopher Manning ir Stanford University
676 Tolerant Retrieval http://web.stanford.edu/class/cs276/handouts/lecture3-tolerant-retrieval-handout-1-per.pdf 1042 2017 Christopher Manning ir Stanford University
677 Evaluation http://web.stanford.edu/class/cs276/handouts/lecture8-evaluation-handout-6-per.pdf 1042 2017 Christopher Manning ir Stanford University
678 Personalization http://web.stanford.edu/class/cs276/handouts/personalization-lecture-6-per-page.pdf 1559 2017 Christopher Manning ir Stanford University
679 Index Construction http://web.stanford.edu/class/cs276/handouts/lecture4-indexconstruction-handout-1-per.pdf 1042 2017 Christopher Manning ir Stanford University
680 Distributed representations http://web.stanford.edu/class/cs276/handouts/lecture20-distributed-representations.pdf 1186 2017 Christopher Manning ir Stanford University
681 BM25, BM25F, and User Behavior http://web.stanford.edu/class/cs276/handouts/lecture12-bm25etc-6up.pdf 1559 2017 Christopher Manning ir Stanford University
682 Learning Ranking http://web.stanford.edu/class/cs276/handouts/lecture14-learning-ranking-6up.pdf 1559 2017 Christopher Manning ir Stanford University
683 Introduction to Information Retrieval http://web.stanford.edu/class/cs276/handouts/lecture12-bm25etc.pdf 1169 2017 Christopher Manning ir Stanford University
684 TF-IDF http://web.stanford.edu/class/cs276/handouts/lecture6-tfidf-handout-6-per.pdf 1559 2017 Christopher Manning ir Stanford University
685 Document Ingestion,Tokenization, Stemming and Lemmatization http://web.stanford.edu/class/cs276/handouts/lecture2-dictionary-handout-6-per.pdf 1042 2017 Christopher Manning ir Stanford University
686 Learning Ranking http://web.stanford.edu/class/cs276/handouts/lecture14-learning-ranking.pdf 1043 2017 Christopher Manning ir Stanford University
687 Evaluation http://web.stanford.edu/class/cs276/handouts/lecture8-evaluation_2014-six-per-page.pdf 1042 2017 Christopher Manning ir Stanford University
688 Web Crawling http://web.stanford.edu/class/cs276/handouts/lecture18-crawling-1-per-page.pdf 1559 2017 Christopher Manning ir Stanford University
689 Spell Correction http://web.stanford.edu/class/cs276/handouts/spell_correction.pdf 1559 2017 Christopher Manning ir Stanford University
690 SVMs http://web.stanford.edu/class/cs276/handouts/lecture13-SVMs.pdf 1038 2017 Christopher Manning ir Stanford University
691 Scoring and results assembly http://web.stanford.edu/class/cs276/handouts/lecture7-vectorspace-6per.pdf 1043 2017 Christopher Manning ir Stanford University
692 Ranking problem http://web.stanford.edu/class/cs276/handouts/lecture5-compression-handout-1-per.pdf 1043 2017 Christopher Manning ir Stanford University
693 Probabilistic Information Retrieval http://web.stanford.edu/class/cs276/handouts/lecture11-probir.pdf 1005 2017 Christopher Manning ir Stanford University
694 Web-QA http://web.stanford.edu/class/cs276/handouts/lecture19-Web-QA-6up.pdf 1031 2017 Christopher Manning ir Stanford University
695 Spell Correction http://web.stanford.edu/class/cs276/handouts/spell_correction-6up.pdf 1559 2017 Christopher Manning ir Stanford University
696 Web-QA http://web.stanford.edu/class/cs276/handouts/lecture19-Web-QA.pdf 1031 2017 Christopher Manning ir Stanford University
697 Scoring and results assembly http://web.stanford.edu/class/cs276/handouts/lecture7-vectorspace-1per.pdf 1043 2017 Christopher Manning ir Stanford University
698 Dictionaries and Tolerant Retrieval http://www.cis.lmu.de/~hs/teach/14s/ir/pdf/03dict.flat.pdf 1042 2014 Hinrich Sch¨utze ir University of Munich
699 Hierarchical Clustering http://www.cis.lmu.de/~hs/teach/14s/ir/pdf/17hier.flat.pdf 1168 2014 Hinrich Sch¨utze ir University of Munich
700 Probabilistic Information Retrieval http://www.cis.lmu.de/~hs/teach/14s/ir/pdf/11prob.flat.pdf 1042 2014 Hinrich Sch¨utze ir University of Munich
701 Learning to Rank http://www.cis.lmu.de/~hs/teach/14s/ir/pdf/15ltr.flat.pdf 1042 2014 Hinrich Sch¨utze ir University of Munich
702 Link Analysis http://www.cis.lmu.de/~hs/teach/14s/ir/pdf/21link.flat.pdf 1176 2014 Hinrich Sch¨utze ir University of Munich
703 Flat Clustering http://www.cis.lmu.de/~hs/teach/14s/ir/pdf/16flat.flat.pdf 1167 2014 Hinrich Sch¨utze ir University of Munich
704 Index Construction http://www.cis.lmu.de/~hs/teach/14s/ir/pdf/04const.flat.pdf 1042 2014 Hinrich Sch¨utze ir University of Munich
705 Vector Space Classification http://www.cis.lmu.de/~hs/teach/14s/ir/pdf/14vcat.flat.pdf 1039 2014 Hinrich Sch¨utze ir University of Munich
706 The term vocabulary and postings lists http://www.cis.lmu.de/~hs/teach/14s/ir/pdf/02voc.flat.pdf 1042 2014 Hinrich Sch¨utze ir University of Munich
707 Latent Semantic Indexing http://www.cis.lmu.de/~hs/teach/14s/ir/pdf/18lsi.flat.pdf 1005 2014 Hinrich Sch¨utze ir University of Munich
708 Language Models for IR http://www.cis.lmu.de/~hs/teach/14s/ir/pdf/12model.flat.pdf 1005 2014 Hinrich Sch¨utze ir University of Munich
709 Scores in a Complete Search System http://www.cis.lmu.de/~hs/teach/14s/ir/pdf/07system.flat.pdf 1171 2014 Hinrich Sch¨utze ir University of Munich
710 Relevance Feedback & Query Expansion http://www.cis.lmu.de/~hs/teach/14s/ir/pdf/09expand.flat.pdf 1174 2014 Hinrich Sch¨utze ir University of Munich
711 Index Compression http://www.cis.lmu.de/~hs/teach/14s/ir/pdf/05comp.flat.pdf 1042 2014 Hinrich Sch¨utze ir University of Munich
712 Crawling http://www.cis.lmu.de/~hs/teach/14s/ir/pdf/20crawl.flat.pdf 1042 2014 Hinrich Sch¨utze ir University of Munich
713 Recap Clustering: Introduction http://www.cis.lmu.de/~hs/teach/14s/ir/pdf/16flat.pdf 1041 2014 Hinrich Sch¨utze ir University of Munich
714 XML Retrieval http://www.cis.lmu.de/~hs/teach/14s/ir/pdf/10xml.flat.pdf 1042 2014 Hinrich Sch¨utze ir University of Munich
715 Scoring, Term Weighting, The Vector Space Model http://www.cis.lmu.de/~hs/teach/14s/ir/pdf/06score.flat.pdf 1171 2014 Hinrich Sch¨utze ir University of Munich
716 Support Vector Machines http://www.cis.lmu.de/~hs/teach/14s/ir/pdf/15svm.flat.pdf 1038 2014 Hinrich Sch¨utze ir University of Munich
717 Evaluation & Result Summaries http://www.cis.lmu.de/~hs/teach/14s/ir/pdf/08eval.flat.pdf 1171 2014 Hinrich Sch¨utze ir University of Munich
718 Text Classification & Naive Bayes http://www.cis.lmu.de/~hs/teach/14s/ir/pdf/13bayes.flat.pdf 1039 2014 Hinrich Sch¨utze ir University of Munich
719 Web Search http://www.cis.lmu.de/~hs/teach/14s/ir/pdf/19web.flat.pdf 1042 2014 Hinrich Sch¨utze ir University of Munich
720 Boolean Retrieval http://www.cis.lmu.de/~hs/teach/14s/ir/pdf/01intro.flat.pdf 1042 2014 Hinrich Sch¨utze ir University of Munich
721 Translation without bilingual parallel corpora http://mt-class.org/penn/slides/20-translation-without-parallel-corpora.pdf 1144 Chris Callison-Burch nlp Johns Kopkins University
722 Syntax-based Translation Part 1: Re-ordering for Phrase-based translation http://mt-class.org/penn/slides/13-reordering-phrase-based-mt.pdf 1144 Chris Callison-Burch nlp Johns Kopkins University
723 Decoding and Inference with Syntactic Translation Models http://mt-class.org/penn/slides/15-decoding-and-inference.pdf 1144 Chris Callison-Burch nlp Johns Kopkins University
724 Harvesng Parallel Documents from the Web http://mt-class.org/penn/slides/16-harvesting-parallel-corpora.pdf 1144 Chris Callison-Burch nlp Johns Kopkins University
725 Course Wrap UpMachine Translation http://mt-class.org/penn/slides/26-course-wrapup.pdf 1144 Chris Callison-Burch nlp Johns Kopkins University
726 Learning and Generating Paraphrases http://mt-class.org/penn/slides/guest-lecture-wei-xu.pdf 1148 Chris Callison-Burch nlp Johns Kopkins University
727 Human Ranking of Machine Translation http://mt-class.org/penn/slides/guest-lecture-matt-post.pdf 1148 Chris Callison-Burch nlp Johns Kopkins University
728 Trees and Forests in Machine Translation http://mt-class.org/penn/slides/guest-lecture-liang-huang.pdf 1144 Chris Callison-Burch nlp Johns Kopkins University
729 Large-scale Paraphrasing for Natural Language Generation http://mt-class.org/penn/slides/18-paraphrasing.pdf 1144 Chris Callison-Burch nlp Johns Kopkins University
730 CRF Word Alignment & Noisy Channel Translation Machine Translation http://mt-class.org/penn/slides/6-discriminative_alignment.pdf 1145 Chris Callison-Burch nlp Johns Kopkins University
731 Faster Decoding for Phrases and Syntax http://mt-class.org/penn/slides/guest-lecture-kenneth-heafield.pdf 1143 Chris Callison-Burch nlp Johns Kopkins University
732 Phrase-Based Machine Translation http://mt-class.org/penn/slides/7-phrase-based-mt.pdf 1148 Chris Callison-Burch nlp Johns Kopkins University
733 Quality Estimation http://mt-class.org/penn/slides/guest-lecture-christian-buck.pdf 1144 Chris Callison-Burch nlp Johns Kopkins University
734 Phrase-Based Machine Translation http://mt-class.org/penn/slides/8-phrase-based-mt-part2.pdf 1148 Chris Callison-Burch nlp Johns Kopkins University
735 Crowdsourcing Translation http://mt-class.org/penn/slides/17-crowdsourcing-translation.pdf 1227 Chris Callison-Burch nlp Johns Kopkins University
736 Introduction to Probability and Statistics http://mt-class.org/penn/slides/2-probability.pdf 1058 Chris Callison-Burch nlp Johns Kopkins University
737 Lexical Translation Models 1 http://mt-class.org/penn/slides/4-lexical_translation.pdf 1147 Chris Callison-Burch nlp Johns Kopkins University
738 Machine Translation http://mt-class.org/penn/slides/1-intro.pdf 1143 Chris Callison-Burch nlp Johns Kopkins University
739 Discriminative Training part 2 http://mt-class.org/penn/slides/12-discriminative-training-part2.pdf 1155 Chris Callison-Burch nlp Johns Kopkins University
740 Language Models Machine Translation http://mt-class.org/penn/slides/3-lm.pdf 1015 Chris Callison-Burch nlp Johns Kopkins University
741 Discriminative Training http://mt-class.org/penn/slides/11-discriminative-training.pdf 1155 Chris Callison-Burch nlp Johns Kopkins University
742 Syntax-based Translation Part 2 http://mt-class.org/penn/slides/14-synchronous-context-free-grammars.pdf 1147 Chris Callison-Burch nlp Johns Kopkins University
743 Evaluating translation quality part 1 http://mt-class.org/penn/slides/9-evaluating-translation-quality.pdf 1148 Chris Callison-Burch nlp Johns Kopkins University
744 Lexical Translation Models http://mt-class.org/penn/slides/5-lexical_translation2.pdf 1147 Chris Callison-Burch nlp Johns Kopkins University
745 Evaluating translation quality part 2 http://mt-class.org/penn/slides/10-evaluating-translation-quality-2.pdf 1148 Chris Callison-Burch nlp Johns Kopkins University
746 Representing Words https://uclanlp.github.io/CS269-17/slides/CS269-06.pdf 1114 2017 Kai-Wei Chang nlp GitHub
747 Learning to Search https://uclanlp.github.io/CS269-17/slides/CS269-08.pdf 1303 2017 Kai-Wei Chang nlp GitHub
748 Multi-Class Classification https://uclanlp.github.io/CS269-17/slides/CS269-03.pdf 1153 2017 Kai-Wei Chang nlp GitHub
749 Structured Prediction Models https://uclanlp.github.io/CS269-17/slides/CS269-04.pdf 1321 2017 Kai-Wei Chang nlp GitHub
750 Binary Classification https://uclanlp.github.io/CS269-17/slides/CS269-02.pdf 1153 2017 Kai-Wei Chang nlp GitHub
751 Introduction to NLP https://uclanlp.github.io/CS269-17/slides/CS269-01.pdf 1009 2017 Kai-Wei Chang nlp GitHub
752 Language Structure: Grammar https://uclanlp.github.io/CS269-17/slides/CS269-07.pdf 1557 2017 Kai-Wei Chang nlp GitHub
753 Semantic Parsing https://uclanlp.github.io/CS269-17/slides/CS269-09.pdf 1218 2017 Kai-Wei Chang nlp GitHub
754 Representation Learning https://uclanlp.github.io/CS269-17/slides/CS269-05.pdf 1114 2017 Kai-Wei Chang nlp GitHub
755 Final Project https://uclanlp.github.io/CS269-17/slides/CS269-Project.pdf 1580 2017 Kai-Wei Chang nlp GitHub
756 Log-Linear Models for Tagging (Maximum-entropy Markov Models) http://www.cs.columbia.edu/~mcollins/cs4705-spring2018/slides/loglinear-tagging-slides.pdf 1085 2018 Michael Collins, nlp Columbia University
757 Tagging Problems, and Hidden Markov Models http://www.cs.columbia.edu/~mcollins/cs4705-spring2018/slides/tagging.pdf 1087 2018 Michael Collins, nlp Columbia University
758 Probabilistic Context-Free Grammars http://www.cs.columbia.edu/~mcollins/cs4705-spring2018/slides/parsing2.pdf 1078 2018 Michael Collins, nlp Columbia University
759 Log-Linear Models for History-Based Parsing http://www.cs.columbia.edu/~mcollins/cs4705-spring2018/slides/loglinear-historybasedparsing.pdf 1094 2018 Michael Collins, nlp Columbia University
760 Word Embeddings in Feedforward Networks, Tagging and Dependency Parsing using Feedforward Networks http://www.cs.columbia.edu/~mcollins/cs4705-spring2018/slides/ff3-slides.pdf 1186 2018 Michael Collins, nlp Columbia University
761 The IBM Translation Models http://www.cs.columbia.edu/~mcollins/cs4705-spring2018/slides/mt2.pdf 1035 2018 Michael Collins, nlp Columbia University
762 Lexicalized Probabilistic Context-Free Grammars http://www.cs.columbia.edu/~mcollins/cs4705-spring2018/slides/parsing3.pdf 1078 2018 Michael Collins, nlp Columbia University
763 Language Modeling http://www.cs.columbia.edu/~mcollins/cs4705-spring2018/slides/lmslides.pdf 1081 2018 Michael Collins, nlp Columbia University
764 Log-Linear Models http://www.cs.columbia.edu/~mcollins/cs4705-spring2018/slides/loglinear-slides.pdf 1160 2018 Michael Collins, nlp Columbia University
765 Recurrent Networks, and Attention for Statistical Machine Translation http://www.cs.columbia.edu/~mcollins/cs4705-spring2018/slides/lstm2-slides.pdf 1190 2018 Michael Collins, nlp Columbia University
766 Parsing, and Context-Free Grammars http://www.cs.columbia.edu/~mcollins/cs4705-spring2018/slides/parsing1.pdf 1078 2018 Michael Collins, nlp Columbia University
767 Feedforward Neural Networks http://www.cs.columbia.edu/~mcollins/cs4705-spring2018/slides/ff-slides.pdf 1006 2018 Michael Collins, nlp Columbia University
768 Computational Graphs, Backpropagation http://www.cs.columbia.edu/~mcollins/cs4705-spring2018/slides/ff2-slides.pdf 1182 2018 Michael Collins, nlp Columbia University
769 Weaknesses of Probabilistic Context-Free Grammars http://www.cs.columbia.edu/~mcollins/cs4705-spring2018/slides/parsing2.2.pdf 1078 2018 Michael Collins, nlp Columbia University
770 Recurrent Networks, and LSTMs, for NLP http://www.cs.columbia.edu/~mcollins/cs4705-spring2018/slides/lstm1-slides.pdf 1190 2018 Michael Collins, nlp Columbia University
771 Introduction and Course Administration http://www.inf.ed.ac.uk/teaching/courses/inf2a/slides2017/inf2a_L01_slides.pdf 1580 2017 Shay Cohen nlp The University of Edinburgh
772 The Complexity of Human Language Informatics http://www.inf.ed.ac.uk/teaching/courses/inf2a/slides2017/inf2a_L27_slides.pdf 1079 2017 Shay Cohen nlp The University of Edinburgh
773 LL(1) grammars: summary Fixing problems with grammars http://www.inf.ed.ac.uk/teaching/courses/inf2a/slides2017/inf2a_L13_slides.pdf 1557 2017 Shay Cohen nlp The University of Edinburgh
774 Agreement, Types and Natural Language Semantics http://www.inf.ed.ac.uk/teaching/courses/inf2a/slides2017/inf2a_L25_slides.pdf 1120 2017 Shay Cohen nlp The University of Edinburgh
775 Morphology parsing http://www.inf.ed.ac.uk/teaching/courses/inf2a/slides2017/inf2a_L15_slides.pdf 1149 2017 Shay Cohen nlp The University of Edinburgh
776 Pushdown automata http://www.inf.ed.ac.uk/teaching/courses/inf2a/slides2017/inf2a_L10_slides.pdf 1078 2017 Shay Cohen nlp The University of Edinburgh
777 Types and Static Type Checking http://www.inf.ed.ac.uk/teaching/courses/inf2a/slides2017/inf2a_L14_slides.pdf 1078 2017 Shay Cohen nlp The University of Edinburgh
778 Lexing Verication of concurrent systems Lexing and other applications http://www.inf.ed.ac.uk/teaching/courses/inf2a/slides2017/inf2a_L07_slides.pdf 1023 2017 Shay Cohen nlp The University of Edinburgh
779 Phrase Structure and Parsing as Search Informatics http://www.inf.ed.ac.uk/teaching/courses/inf2a/slides2017/inf2a_L19_slides.pdf 1094 2017 Shay Cohen nlp The University of Edinburgh
780 Fun with weighted FSTs Informatics http://www.inf.ed.ac.uk/teaching/courses/inf2a/slides2017/inf2a_L18_slides.pdf 1016 2017 Shay Cohen nlp The University of Edinburgh
781 What Is Inf2a about? http://www.inf.ed.ac.uk/teaching/courses/inf2a/slides2017/inf2a_L02_slides.pdf 1000 2017 Shay Cohen nlp The University of Edinburgh
782 An Earley Parsing Example http://www.inf.ed.ac.uk/teaching/courses/inf2a/slides2017/earley-example.pdf 1096 2017 Shay Cohen nlp The University of Edinburgh
783 Context-sensitive languages http://www.inf.ed.ac.uk/teaching/courses/inf2a/slides2017/inf2a_L29_slides.pdf 1078 2017 Shay Cohen nlp The University of Edinburgh
784 CKY Algorithm http://www.inf.ed.ac.uk/teaching/courses/inf2a/slides2017/inf2a_L20_slides.pdf 1095 2017 Shay Cohen nlp The University of Edinburgh
785 Part-of-Speech Tagging Informatics http://www.inf.ed.ac.uk/teaching/courses/inf2a/slides2017/inf2a_L17_slides.pdf 1017 2017 Shay Cohen nlp The University of Edinburgh
786 Generating parse tables http://www.inf.ed.ac.uk/teaching/courses/inf2a/slides2017/inf2a_L12_slides.pdf 1017 2017 Shay Cohen nlp The University of Edinburgh
787 Earley Parsing http://www.inf.ed.ac.uk/teaching/courses/inf2a/slides2017/inf2a_L21_slides.pdf 1019 2017 Shay Cohen nlp The University of Edinburgh
788 Computing Natural Language Semantics Informatics http://www.inf.ed.ac.uk/teaching/courses/inf2a/slides2017/inf2a_L26_slides.pdf 1120 2017 Shay Cohen nlp The University of Edinburgh
789 Turing machines http://www.inf.ed.ac.uk/teaching/courses/inf2a/slides2017/inf2a_L31_slides.pdf 1300 2017 Shay Cohen nlp The University of Edinburgh
790 Revision Lecture http://www.inf.ed.ac.uk/teaching/courses/inf2a/slides2017/inf2a_L32_slides.pdf 1580 2017 Shay Cohen nlp The University of Edinburgh
791 String and pattern matching and Applications to string and pattern matching http://www.inf.ed.ac.uk/teaching/courses/inf2a/slides2017/inf2a_L06_slides.pdf 1003 2017 Shay Cohen nlp The University of Edinburgh
792 Constructions on Finite Automata http://www.inf.ed.ac.uk/teaching/courses/inf2a/slides2017/inf2a_L04_slides.pdf 1065 2017 Shay Cohen nlp The University of Edinburgh
793 Regular expressions and Kleene’s theorem http://www.inf.ed.ac.uk/teaching/courses/inf2a/slides2017/inf2a_L05_slides.pdf 1074 2017 Shay Cohen nlp The University of Edinburgh
794 Predictive parsing http://www.inf.ed.ac.uk/teaching/courses/inf2a/slides2017/inf2a_L11_slides.pdf 1094 2017 Shay Cohen nlp The University of Edinburgh
795 Languages and Automata http://www.inf.ed.ac.uk/teaching/courses/inf2a/slides2017/inf2a_L03_slides.pdf 1065 2017 Shay Cohen nlp The University of Edinburgh
796 Turing machines and linear bounded automata http://www.inf.ed.ac.uk/teaching/courses/inf2a/slides2017/inf2a_L30_slides.pdf 1300 2017 Shay Cohen nlp The University of Edinburgh
797 Dening languages via grammars http://www.inf.ed.ac.uk/teaching/courses/inf2a/slides2017/inf2a_L09_slides.pdf 1557 2017 Shay Cohen nlp The University of Edinburgh
798 Parameter Estimation and Lexicalisation for PCFGs Informatics http://www.inf.ed.ac.uk/teaching/courses/inf2a/slides2017/inf2a_L23_slides.pdf 1098 2017 Shay Cohen nlp The University of Edinburgh
799 What is programming language semantics? http://www.inf.ed.ac.uk/teaching/courses/inf2a/slides2017/inf2a_L28_slides.pdf 1065 2017 Shay Cohen nlp The University of Edinburgh
800 limitations of regular languages http://www.inf.ed.ac.uk/teaching/courses/inf2a/slides2017/inf2a_L08_slides.pdf 1065 2017 Shay Cohen nlp The University of Edinburgh
801 Parts-of-speech and the Lexicon in Natural Language http://www.inf.ed.ac.uk/teaching/courses/inf2a/slides2017/inf2a_L16_slides.pdf 1017 2017 Shay Cohen nlp The University of Edinburgh
802 Probabilistic Context-Free Grammars http://www.inf.ed.ac.uk/teaching/courses/inf2a/slides2017/inf2a_L22_slides.pdf 1098 2017 Shay Cohen nlp The University of Edinburgh
803 HMMs https://courses.cs.washington.edu/courses/cse447/18wi/slides/HMMs.pdf 1016 2018 Yejin Choi nlp University of Washington
804 Language Models https://courses.cs.washington.edu/courses/cse447/18wi/slides/LanguageModels.pdf 1081 2018 Yejin Choi nlp University of Washington
805 Log Linear Models https://courses.cs.washington.edu/courses/cse447/18wi/slides/LogLinear.pdf 1160 2018 Yejin Choi nlp University of Washington
806 Parsing https://courses.cs.washington.edu/courses/cse447/18wi/slides/Parsing.pdf 1093 2018 Yejin Choi nlp University of Washington
807 Distributed Semantics https://courses.cs.washington.edu/courses/cse447/18wi/slides/DistributedSemantics.pdf 1120 2018 Yejin Choi nlp University of Washington
808 Machine Translation https://courses.cs.washington.edu/courses/cse447/18wi/slides/MT.pdf 1143 2018 Yejin Choi nlp University of Washington
809 Deep Learning https://courses.cs.washington.edu/courses/cse447/18wi/slides/DeepLearning.pdf 1181 2018 Yejin Choi nlp University of Washington
810 Dependency Parsing And Other Grammar Formalisms https://courses.cs.washington.edu/courses/cse447/18wi/slides/Parsing-dep.pdf 1282 2018 Yejin Choi nlp University of Washington
811 Greedy Algorithm https://courses.cs.washington.edu/courses/cse447/18wi/slides/edmond.pdf 1564 2018 Yejin Choi nlp University of Washington
812 Frame Semantics https://courses.cs.washington.edu/courses/cse447/18wi/slides/Frame.pdf 1120 2018 Yejin Choi nlp University of Washington
813 Natural Language Processing Introduction https://courses.cs.washington.edu/courses/cse447/18wi/slides/Intro.pdf 1009 2018 Yejin Choi nlp University of Washington
814 Application of Bayes Theorem http://www.inf.ed.ac.uk/teaching/courses/fmcs1/slides/lecture19.pdf 1059 2016 Frank Keller nlp University of Edinburgh
815 Continuous Random Variables and Density 2 http://www.inf.ed.ac.uk/teaching/courses/fmcs1/slides/lecture21-2x2.pdf 1059 2016 Frank Keller nlp University of Edinburgh
816 Noisy Channel Model and Applications http://www.inf.ed.ac.uk/teaching/courses/fmcs1/slides/lecture29.pdf 1084 2016 Frank Keller nlp University of Edinburgh
817 Distributions Independence http://www.inf.ed.ac.uk/teaching/courses/fmcs1/slides/lecture20-2x2.pdf 1059 2016 Frank Keller nlp University of Edinburgh
818 What is Probability Theory? Probability and Cognition Combinatorial Methods 2 http://www.inf.ed.ac.uk/teaching/courses/fmcs1/slides/lecture16-2x2.pdf 1058 2016 Frank Keller nlp University of Edinburgh
819 Entropy Rate, Mutual Information http://www.inf.ed.ac.uk/teaching/courses/fmcs1/slides/lecture26.pdf 1065 2016 Frank Keller nlp University of Edinburgh
820 Formal Modeling in Cognitive Science: introduction http://www.inf.ed.ac.uk/teaching/courses/fmcs1/slides/lecture00.pdf 1052 2016 Frank Keller nlp University of Edinburgh
821 Kraft Inequality, Source Coding Theorem, Huffman Coding http://www.inf.ed.ac.uk/teaching/courses/fmcs1/slides/lecture28.pdf 1065 2016 Frank Keller nlp University of Edinburgh
822 Sample Spaces and Events Probability of an Event http://www.inf.ed.ac.uk/teaching/courses/fmcs1/slides/lecture17.pdf 1059 2016 Frank Keller nlp University of Edinburgh
823 Application of Bayes Theorem 2 http://www.inf.ed.ac.uk/teaching/courses/fmcs1/slides/lecture19-2x2.pdf 1059 2016 Frank Keller nlp University of Edinburgh
824 Expectation and Related Concepts Chebyshevs Theorem http://www.inf.ed.ac.uk/teaching/courses/fmcs1/slides/lecture22-2x2.pdf 1065 2016 Frank Keller nlp University of Edinburgh
825 Continuous Random Variables and Density http://www.inf.ed.ac.uk/teaching/courses/fmcs1/slides/lecture21.pdf 1059 2016 Frank Keller nlp University of Edinburgh
826 What is Probability Theory? Probability and Cognition Combinatorial Methods http://www.inf.ed.ac.uk/teaching/courses/fmcs1/slides/lecture16.pdf 1058 2016 Frank Keller nlp University of Edinburgh
827 Sample Spaces and Events Probability of an Event http://www.inf.ed.ac.uk/teaching/courses/fmcs1/slides/lecture17-2x2.pdf 1059 2016 Frank Keller nlp University of Edinburgh
828 Conditional Probability and Independence Bayes Theorem http://www.inf.ed.ac.uk/teaching/courses/fmcs1/slides/lecture18.pdf 1060 2016 Frank Keller nlp University of Edinburgh
829 Entropy http://www.inf.ed.ac.uk/teaching/courses/fmcs1/slides/lecture25.pdf 1065 2016 Frank Keller nlp University of Edinburgh
830 Expectation and Related Concepts Chebyshevs Theorem http://www.inf.ed.ac.uk/teaching/courses/fmcs1/slides/lecture22.pdf 1065 2016 Frank Keller nlp University of Edinburgh
831 Bayesian Cognitive Science Cognitive http://www.inf.ed.ac.uk/teaching/courses/fmcs1/slides/lecture24b.pdf 1059 2016 Frank Keller nlp University of Edinburgh
832 Special Probability Distributions Special Probability Densities http://www.inf.ed.ac.uk/teaching/courses/fmcs1/slides/lecture23-2x2.pdf 1059 2016 Frank Keller nlp University of Edinburgh
833 Application: Discovering Collocations http://www.inf.ed.ac.uk/teaching/courses/fmcs1/slides/lecture27.pdf 1118 2016 Frank Keller nlp University of Edinburgh
834 Conditional Probability and Independence Bayesian Theorem Formal Modeling in Cognitive Science http://www.inf.ed.ac.uk/teaching/courses/fmcs1/slides/lecture18-2x2.pdf 1003 2016 Frank Keller nlp University of Edinburgh
835 Distributions Independence http://www.inf.ed.ac.uk/teaching/courses/fmcs1/slides/lecture20.pdf 1059 2016 Frank Keller nlp University of Edinburgh
836 Special Probability Distributions Special Probability Densities http://www.inf.ed.ac.uk/teaching/courses/fmcs1/slides/lecture23.pdf 1059 2016 Frank Keller nlp University of Edinburgh
837 Lagrangian Relaxa,on for MAP Inference http://demo.clab.cs.cmu.edu/fa2015-11763/slides/lagrangian-relaxation.pdf 1217 2015 Chris Dyer nlp Carnegie Mellon University
838 Decoding http://demo.clab.cs.cmu.edu/fa2015-11763/slides/decoding-1.pdf 1084 2015 Chris Dyer nlp Carnegie Mellon University
839 Spectral Learning http://demo.clab.cs.cmu.edu/fa2015-11763/slides/spectral_slides.pdf 1264 2015 Chris Dyer nlp Carnegie Mellon University
840 Probability Distributions on Structured Objects http://demo.clab.cs.cmu.edu/fa2015-11763/slides/probability.pdf 1321 2015 Chris Dyer nlp Carnegie Mellon University
841 Minimum Bayes Risk http://demo.clab.cs.cmu.edu/fa2015-11763/slides/mbr.pdf 1060 2015 Chris Dyer nlp Carnegie Mellon University
842 Approximate Inference: Randomized Methods http://demo.clab.cs.cmu.edu/fa2015-11763/slides/random-search.pdf 1217 2015 Chris Dyer nlp Carnegie Mellon University
843 Structured Prediction for Language and Other Discrete Data http://demo.clab.cs.cmu.edu/fa2015-11763/slides/intro.pdf 1321 2015 Chris Dyer nlp Carnegie Mellon University
844 Experimentation http://demo.clab.cs.cmu.edu/fa2015-11763/slides/experimentation.pdf 1559 2015 Chris Dyer nlp Carnegie Mellon University
845 HMM Review http://demo.clab.cs.cmu.edu/fa2015-11763/slides/hmm-review-2.pdf 1016 2015 Chris Dyer nlp Carnegie Mellon University
846 Learning Generative Models http://demo.clab.cs.cmu.edu/fa2015-11763/slides/gen-learning1.pdf 1165 2015 Chris Dyer nlp Carnegie Mellon University
847 Sequence labeling: HMMs http://demo.clab.cs.cmu.edu/fa2015-11763/slides/09-27-stacking+searn.pdf 1016 2015 Chris Dyer nlp Carnegie Mellon University
848 Neural Networks in Structured Prediction 2 http://demo.clab.cs.cmu.edu/fa2015-11763/slides/neural2.pdf 1181 2015 Chris Dyer nlp Carnegie Mellon University
849 Empirical Risk Minimization http://demo.clab.cs.cmu.edu/fa2015-11763/slides/erm.pdf 1066 2015 Chris Dyer nlp Carnegie Mellon University
850 Structure and Support Vector Machines http://demo.clab.cs.cmu.edu/fa2015-11763/slides/ssvm.pdf 1038 2015 Chris Dyer nlp Carnegie Mellon University
851 Conditional Models http://demo.clab.cs.cmu.edu/fa2015-11763/slides/conditional-models.pdf 1058 2015 Chris Dyer nlp Carnegie Mellon University
852 Neural Networks in Structured Prediction http://demo.clab.cs.cmu.edu/fa2015-11763/slides/neural1.pdf 1181 2015 Chris Dyer nlp Carnegie Mellon University
853 HMM Review http://demo.clab.cs.cmu.edu/fa2015-11763/slides/hmm-review-1.pdf 1016 2015 Chris Dyer nlp Carnegie Mellon University
854 Natural Language Parsing with Context-Free Grammars http://demo.clab.cs.cmu.edu/fa2015-11763/slides/cfparsing.pdf 1078 2015 Chris Dyer nlp Carnegie Mellon University
855 Natural Language Dependency Parsing http://demo.clab.cs.cmu.edu/fa2015-11763/slides/depparsing.pdf 1282 2015 Chris Dyer nlp Carnegie Mellon University
856 How much linguistics is needed for NLP? http://demo.clab.cs.cmu.edu/fa2015-11763/slides/HowMuchLinguistics2015.pdf 1009 2015 Chris Dyer nlp Carnegie Mellon University
857 Decoding 2 http://demo.clab.cs.cmu.edu/fa2015-11763/slides/decoding-2.pdf 1084 2015 Chris Dyer nlp Carnegie Mellon University
858 Writng Well in NLP and ML http://demo.clab.cs.cmu.edu/fa2015-11763/slides/writing.pdf 1009 2015 Chris Dyer nlp Carnegie Mellon University
859 Soft Inference and Posterior Probabilities http://demo.clab.cs.cmu.edu/fa2015-11763/slides/softinference.pdf 1217 2015 Chris Dyer nlp Carnegie Mellon University
860 Neural network http://www.mathcs.emory.edu/~choi/courses/cs571/slides/neural_network.pdf 1181 2017 Jinho D. Choi nlp Emory University
861 part of speech tagging http://www.mathcs.emory.edu/~choi/courses/cs571/slides/part_of_speech_tagging.pdf 1085 2017 Jinho D. Choi nlp Emory University
862 stocastic gradient descent http://www.mathcs.emory.edu/~choi/courses/cs571/slides/stocastic_gradient_descent.pdf 1182 2017 Jinho D. Choi nlp Emory University
863 Named entity recognition http://www.mathcs.emory.edu/~choi/courses/cs571/slides/named_entity_recognition.pdf 1089 2017 Jinho D. Choi nlp Emory University
864 Introduction to Natural Language Processing http://www.mathcs.emory.edu/~choi/courses/cs571/slides/introduction.pdf 1009 2017 Jinho D. Choi nlp Emory University
865 Distributional semantics http://www.mathcs.emory.edu/~choi/courses/cs571/slides/distributional_semantics.pdf 1120 2017 Jinho D. Choi nlp Emory University
866 Sentiment analysis http://www.mathcs.emory.edu/~choi/courses/cs571/slides/sentiment_analysis.pdf 1122 2017 Jinho D. Choi nlp Emory University
867 Dependency parsing http://www.mathcs.emory.edu/~choi/courses/cs571/slides/dependency_parsing.pdf 1282 2017 Jinho D. Choi nlp Emory University
868 Semantic role labeling http://www.mathcs.emory.edu/~choi/courses/cs571/slides/semantic_role_labeling.pdf 1220 2017 Jinho D. Choi nlp Emory University
869 Regression for classification http://www.mathcs.emory.edu/~choi/courses/cs571/slides/regression_methods.pdf 1158 2017 Jinho D. Choi nlp Emory University
870 Classification http://people.ischool.berkeley.edu/~dbamman/nlpF17/slides/2_classification_1.pdf 1153 2017 David Bamman nlp UC Berkeley
871 POS http://people.ischool.berkeley.edu/~dbamman/nlpF17/slides/9_POS.pdf 1085 2017 David Bamman nlp UC Berkeley
872 Truth ethics http://people.ischool.berkeley.edu/~dbamman/nlpF17/slides/5_truth_ethics.pdf 1065 2017 David Bamman nlp UC Berkeley
873 Vector Semantics http://people.ischool.berkeley.edu/~dbamman/nlpF17/slides/8_vector_semantics.pdf 1115 2017 David Bamman nlp UC Berkeley
874 Classification 2 http://people.ischool.berkeley.edu/~dbamman/nlpF17/slides/3_classification_2.pdf 1153 2017 David Bamman nlp UC Berkeley
875 Classification 3 http://people.ischool.berkeley.edu/~dbamman/nlpF17/slides/4_classification_3.pdf 1153 2017 David Bamman nlp UC Berkeley
876 Features and hypothesis tests http://people.ischool.berkeley.edu/~dbamman/nlpF17/slides/12_significance.pdf 1163 2017 David Bamman nlp UC Berkeley
877 LSTM http://people.ischool.berkeley.edu/~dbamman/nlpF17/slides/11_crf_lstm.pdf 1191 2017 David Bamman nlp UC Berkeley
878 Language models 1 http://people.ischool.berkeley.edu/~dbamman/nlpF17/slides/6_LM_1.pdf 1015 2017 David Bamman nlp UC Berkeley
879 Language models 2 http://people.ischool.berkeley.edu/~dbamman/nlpF17/slides/7_LM_2.pdf 1015 2017 David Bamman nlp UC Berkeley
880 Sequence Labeling 1 http://people.ischool.berkeley.edu/~dbamman/nlpF17/slides/10_hmm_memm.pdf 1016 2017 David Bamman nlp UC Berkeley
881 Natural Language Processing Introduction http://people.ischool.berkeley.edu/~dbamman/nlpF17/slides/1_intro.pdf 1009 2017 David Bamman nlp UC Berkeley
882 Discriminative Sequence http://people.cs.georgetown.edu/nschneid/cosc572/s18/14_discriminative-sequence.pdf 1155 2018 Nathan Schneider nlp Georgetown University
883 What is Linguistics? http://people.cs.georgetown.edu/nschneid/cosc572/s18/03_ling.pdf 1012 2018 Nathan Schneider nlp Georgetown University
884 Algorithms for Natural Language Processing http://people.cs.georgetown.edu/nschneid/cosc572/s18/08a_lexsem_slides.pdf 1009 2018 Nathan Schneider nlp Georgetown University
885 Language Models: More smoothing http://people.cs.georgetown.edu/nschneid/cosc572/s18/06_LM2_slides.pdf 1082 2018 Nathan Schneider nlp Georgetown University
886 Linear Models for Classication: Features & Weights http://people.cs.georgetown.edu/nschneid/cosc572/s18/08b_discriminative-features.pdf 1037 2018 Nathan Schneider nlp Georgetown University
887 Parts of Speech http://people.cs.georgetown.edu/nschneid/cosc572/s18/10_pos_slides.pdf 1085 2018 Nathan Schneider nlp Georgetown University
888 A note about NLP tools NLTK http://people.cs.georgetown.edu/nschneid/cosc572/s18/19_srl_slides.pdf 1065 2018 Nathan Schneider nlp Georgetown University
889 Context in Language http://people.cs.georgetown.edu/nschneid/cosc572/s18/23_context.pdf 1313 2018 Nathan Schneider nlp Georgetown University
890 Coreference Resolution http://people.cs.georgetown.edu/nschneid/cosc572/s18/18_coref_slides.pdf 1065 2018 Nathan Schneider nlp Georgetown University
891 Machine Translation http://people.cs.georgetown.edu/nschneid/cosc572/s18/20_MT_slides.pdf 1143 2018 Nathan Schneider nlp Georgetown University
892 Linear Models for Classication: Discriminative Learning (Perceptron, SVMs, MaxEnt) http://people.cs.georgetown.edu/nschneid/cosc572/s18/09_discriminative-learning.pdf 1161 2018 Nathan Schneider nlp Georgetown University
893 English Syntax & CFGs http://people.cs.georgetown.edu/nschneid/cosc572/s18/15_syntax_slides.pdf 1078 2018 Nathan Schneider nlp Georgetown University
894 Text Corpora http://people.cs.georgetown.edu/nschneid/cosc572/s18/02a_slides.pdf 1313 2018 Nathan Schneider nlp Georgetown University
895 Parsing with Context Free Grammars http://people.cs.georgetown.edu/nschneid/cosc572/s18/16_parsing_slides.pdf 1078 2018 Nathan Schneider nlp Georgetown University
896 Wrapping Up http://people.cs.georgetown.edu/nschneid/cosc572/s18/24_wrapup.pdf 1009 2018 Nathan Schneider nlp Georgetown University
897 Basic Text Processing Regular Expressions http://people.cs.georgetown.edu/nschneid/cosc572/s18/02b_TextProc.pdf 1074 2018 Nathan Schneider nlp Georgetown University
898 Deep Learning & Neural Networks http://people.cs.georgetown.edu/nschneid/cosc572/s18/22_deep_learning.pdf 1181 2018 Nathan Schneider nlp Georgetown University
899 Part-of-speech tagging and HMMs http://people.cs.georgetown.edu/nschneid/cosc572/s18/11_pos_hmm_slides.pdf 1016 2018 Nathan Schneider nlp Georgetown University
900 N-gram Language Models http://people.cs.georgetown.edu/nschneid/cosc572/s18/05_LM_slides.pdf 1081 2018 Nathan Schneider nlp Georgetown University
901 Dependency Parsing http://people.cs.georgetown.edu/nschneid/cosc572/s18/17_dependency-parsing_slides.pdf 1282 2018 Nathan Schneider nlp Georgetown University
902 Annotation http://people.cs.georgetown.edu/nschneid/cosc572/s18/13_annotation_slides.pdf 1013 2018 Nathan Schneider nlp Georgetown University
903 Natural Language Processing Introduction http://people.cs.georgetown.edu/nschneid/cosc572/s18/01_slides.pdf 1009 2018 Nathan Schneider nlp Georgetown University
904 Distributional Semantics http://people.cs.georgetown.edu/nschneid/cosc572/s18/21_distributional_slides.pdf 1120 2018 Nathan Schneider nlp Georgetown University
905 Classication: Nave Bayes http://people.cs.georgetown.edu/nschneid/cosc572/s18/07_NB_classification.pdf 1122 2018 Nathan Schneider nlp Georgetown University
906 Algorithms for HMMs http://people.cs.georgetown.edu/nschneid/cosc572/s18/12_viterbi_slides.pdf 1016 2018 Nathan Schneider nlp Georgetown University
907 Project discussion http://people.cs.umass.edu/~brenocon/inlp2015/10-projects.pdf 1580 2015 Brendan O’Connor nlp Umass University
908 Natural Language Processing Introduction http://people.cs.umass.edu/~brenocon/inlp2015/05-phonotactics-jarosz-cs585-guestlecture-20150925.pdf 1009 2015 Brendan O’Connor nlp Umass University
909 Machine Translation Part 3, Alternatives & Evaluation http://people.cs.umass.edu/~brenocon/inlp2015/15-mt-more.pdf 1143 2015 Brendan O’Connor nlp Umass University
910 Probability, Naive Bayes http://people.cs.umass.edu/~brenocon/inlp2015/02-probnb.pdf 1058 2015 Brendan O’Connor nlp Umass University
911 CKY Algorithm http://people.cs.umass.edu/~brenocon/inlp2015/17-cky.pdf 1095 2015 Brendan O’Connor nlp Umass University
912 NLP Evaluation: Bootstrapping & sig tests http://people.cs.umass.edu/~brenocon/inlp2015/16-sigtests.pdf 1071 2015 Brendan O’Connor nlp Umass University
913 Lecture 1: Course Introduction http://people.cs.umass.edu/~brenocon/inlp2015/01-intro.pdf 1580 2015 Brendan O’Connor nlp Umass University
914 Parts of speech http://people.cs.umass.edu/~brenocon/inlp2015/06-pos.pdf 1085 2015 Brendan O’Connor nlp Umass University
915 Machine Translation Part 2, and the EM Algorithm http://people.cs.umass.edu/~brenocon/inlp2015/14-mt-em.pdf 1143 2015 Brendan O’Connor nlp Umass University
916 Classification http://people.cs.umass.edu/~brenocon/inlp2015/03-classif.pdf 1037 2015 Brendan O’Connor nlp Umass University
917 CKY Algorithm 2 http://people.cs.umass.edu/~brenocon/inlp2015/18-cky.pdf 1095 2015 Brendan O’Connor nlp Umass University
918 Coreference and Entity Resolution http://people.cs.umass.edu/~brenocon/inlp2015/19-coref.pdf 1065 2015 Brendan O’Connor nlp Umass University
919 Logistic Regression http://people.cs.umass.edu/~brenocon/inlp2015/04-logreg.pdf 1158 2015 Brendan O’Connor nlp Umass University
920 Log-linear models and CRFs http://people.cs.umass.edu/~brenocon/inlp2015/08-discseq.pdf 1160 2015 Brendan O’Connor nlp Umass University
921 Machine Translation (Part 1) http://people.cs.umass.edu/~brenocon/inlp2015/13-mt.pdf 1143 2015 Brendan O’Connor nlp Umass University
922 Viterbi Algorithm http://people.cs.umass.edu/~brenocon/inlp2015/10-boardphotos.pdf 1314 2015 Brendan O’Connor nlp Umass University
923 Syntactic Dependencies http://people.cs.umass.edu/~brenocon/inlp2015/19-deps.pdf 1281 2015 Brendan O’Connor nlp Umass University
924 CRF and Structured Perceptron http://people.cs.umass.edu/~brenocon/inlp2015/09-discseq-perc.pdf 1157 2015 Brendan O’Connor nlp Umass University
925 NLP Evaluation http://people.cs.umass.edu/~brenocon/inlp2015/15-eval.pdf 1071 2015 Brendan O’Connor nlp Umass University
926 Distributional Semantics http://people.cs.umass.edu/~brenocon/inlp2015/22-distsim.pdf 1120 2015 Brendan O’Connor nlp Umass University
927 Lexical Semantics http://people.cs.umass.edu/~brenocon/inlp2015/21-lexsem.pdf 1023 2015 Brendan O’Connor nlp Umass University
928 Edit Distance, Spelling Correction, and the Noisy Channel http://people.cs.umass.edu/~brenocon/inlp2015/06-lecnotes.pdf 1084 2015 Brendan O’Connor nlp Umass University
929 Coreference and Entity Resolution http://people.cs.umass.edu/~brenocon/inlp2015/20-coref.pdf 1065 2015 Brendan O’Connor nlp Umass University
930 Edit Distance, Spelling Correction, and the Noisy Channel http://people.cs.umass.edu/~brenocon/inlp2015/12-editdist.pdf 1084 2015 Brendan O’Connor nlp Umass University
931 Introduction to Natural Language Processing http://people.cs.umass.edu/~brenocon/inlp2015/16-syntax.pdf 1009 2015 Brendan O’Connor nlp Umass University
932 More Word Vectors http://cs224d.stanford.edu/lectures/CS224d-Lecture3.pdf 1186 2016 Richard Socher nlp Stanford University
933 Recap, Projects and Fancy Recurrent Neural Networks for Machine Translation http://cs224d.stanford.edu/lectures/CS224d-Lecture9.pdf 1143 2016 Richard Socher nlp Stanford University
934 Sequence to Sequence Learning http://cs224d.stanford.edu/lectures/CS224d-Lecture16.pdf 1186 2016 Richard Socher nlp Stanford University
935 Word Window Classification and Neural Networks http://cs224d.stanford.edu/lectures/CS224d-Lecture4.pdf 1048 2016 Richard Socher nlp Stanford University
936 Neural Networks in Speech Recognition http://cs224d.stanford.edu/lectures/CS224d-Lecture14.pdf 1049 2016 Richard Socher nlp Stanford University
937 Neural Machine Translation http://cs224d.stanford.edu/lectures/CS224d-Lecture15.pdf 1197 2016 Richard Socher nlp Stanford University
938 Advanced Recursive Neural Networks http://cs224d.stanford.edu/lectures/CS224d-Lecture11.pdf 1192 2016 Richard Socher nlp Stanford University
939 Word Vectors http://cs224d.stanford.edu/lectures/CS224d-Lecture2.pdf 1186 2016 Richard Socher nlp Stanford University
940 Recurrent Neural Networks http://cs224d.stanford.edu/lectures/CS224d-Lecture8.pdf 1190 2016 Richard Socher nlp Stanford University
941 Wrap up: LSTMs and Recursive Neural Networks http://cs224d.stanford.edu/lectures/CS224d-Lecture10.pdf 1191 2016 Richard Socher nlp Stanford University
942 Project Information Neural Networks & Backprop http://cs224d.stanford.edu/lectures/CS224d-Lecture5.pdf 1006 2016 Richard Socher nlp Stanford University
943 AWS Tutorial http://cs224d.stanford.edu/lectures/CS224D-Lecture7-2.pdf 1189 2016 Richard Socher nlp Stanford University
944 Convolutional Neural Networks (for NLP) http://cs224d.stanford.edu/lectures/CS224d-Lecture13.pdf 1193 2016 Richard Socher nlp Stanford University
945 Deep Learning for Natural Language Processing: Introductioin http://cs224d.stanford.edu/lectures/CS224d-Lecture1.pdf 1009 2016 Richard Socher nlp Stanford University
946 Dynamic Memory Networks for Question Answering over Text and Images http://cs224d.stanford.edu/lectures/CS224d-Lecture17.pdf 1199 2016 Richard Socher nlp Stanford University
947 Midterm Review http://cs224d.stanford.edu/lectures/CS224d-Lecture12.pdf 1580 2016 Richard Socher nlp Stanford University
948 TensorFlow Tutorial http://cs224d.stanford.edu/lectures/CS224d-Lecture7.pdf 1189 2016 Richard Socher nlp Stanford University
949 Neural Tips and Tricks Recurrent Neural Networks http://cs224d.stanford.edu/lectures/CS224d-Lecture6.pdf 1182 2016 Richard Socher nlp Stanford University
950 Loss-augmented Structured Prediction http://www.cs.umd.edu/class/fall2017/cmsc723/slides/slides_11.pdf 1262 2017 Marine Carpuat nlp University of Maryland
951 Vector Semantics Dense Vectors http://www.cs.umd.edu/class/fall2017/cmsc723/slides/slides_06.pdf 1115 2017 Marine Carpuat nlp University of Maryland
952 Word Meaning: Distributional Representations and Word Sense Disambiguation http://www.cs.umd.edu/class/fall2017/cmsc723/slides/slides_02.pdf 1186 2017 Marine Carpuat nlp University of Maryland
953 Phrase-Based Machine Translation http://www.cs.umd.edu/class/fall2017/cmsc723/slides/slides_19.pdf 1148 2017 Marine Carpuat nlp University of Maryland
954 Introduction to Machine Translation http://www.cs.umd.edu/class/fall2017/cmsc723/slides/slides_15.pdf 1143 2017 Marine Carpuat nlp University of Maryland
955 Text Classification & Linear Models http://www.cs.umd.edu/class/fall2017/cmsc723/slides/slides_03.pdf 1039 2017 Marine Carpuat nlp University of Maryland
956 Sequence to Sequence Models for Machine Translation 2 http://www.cs.umd.edu/class/fall2017/cmsc723/slides/slides_17.pdf 1186 2017 Marine Carpuat nlp University of Maryland
957 Computational Linguistics http://www.cs.umd.edu/class/fall2017/cmsc723/slides/slides_01.pdf 1067 2017 Marine Carpuat nlp University of Maryland
958 POS tagging http://www.cs.umd.edu/class/fall2017/cmsc723/slides/slides_09.pdf 1085 2017 Marine Carpuat nlp University of Maryland
959 Dependency Parsing 2 http://www.cs.umd.edu/class/fall2017/cmsc723/slides/slides_13.pdf 1282 2017 Marine Carpuat nlp University of Maryland
960 Sequence to Sequence Models for Machine Translation http://www.cs.umd.edu/class/fall2017/cmsc723/slides/slides_16.pdf 1186 2017 Marine Carpuat nlp University of Maryland
961 Logistic Regression & Neural Networks http://www.cs.umd.edu/class/fall2017/cmsc723/slides/slides_05.pdf 1158 2017 Marine Carpuat nlp University of Maryland
962 Alignment in Machine Translation http://www.cs.umd.edu/class/fall2017/cmsc723/slides/slides_18.pdf 1144 2017 Marine Carpuat nlp University of Maryland
963 From Dependency Parsing to Imitation Learning http://www.cs.umd.edu/class/fall2017/cmsc723/slides/slides_14.pdf 1545 2017 Marine Carpuat nlp University of Maryland
964 POS tagging http://www.cs.umd.edu/class/fall2017/cmsc723/slides/slides_10.pdf 1282 2017 Marine Carpuat nlp University of Maryland
965 Linear Models Continued: Perceptron & Logistic Regression http://www.cs.umd.edu/class/fall2017/cmsc723/slides/slides_04.pdf 1337 2017 Marine Carpuat nlp University of Maryland
966 Language Modeling Introduction http://www.cs.umd.edu/class/fall2017/cmsc723/slides/slides_07.pdf 1081 2017 Marine Carpuat nlp University of Maryland
967 Dependency Parsing http://www.cs.umd.edu/class/fall2017/cmsc723/slides/slides_12.pdf 1282 2017 Marine Carpuat nlp University of Maryland
968 Shallow Semantics https://github.com/shichao116/NLP/blob/master/slides.2014/srl-slides.pdf 1023 2014 Jacob Eisenstein nlp GitHub
969 Distributional Lexical Semantics https://github.com/shichao116/NLP/blob/master/slides.2014/distributional-slides.pdf 1023 2014 Jacob Eisenstein nlp GitHub
970 Naïve Bayes,Perceptron https://github.com/shichao116/NLP/blob/master/slides.2014/lec3-slides.pdf 1157 2014 Jacob Eisenstein nlp GitHub
971 Introduction to Natural Language Processing https://github.com/shichao116/NLP/blob/master/slides.2014/lec1.pdf 1009 2014 Jacob Eisenstein nlp GitHub
972 Morphology https://github.com/shichao116/NLP/blob/master/slides.2014/lec7-morphology-slides.pdf 1069 2014 Jacob Eisenstein nlp GitHub
973 Examples of Viterbi, Forward, and Backward https://github.com/shichao116/NLP/blob/master/slides.2014/sequence-slides.pdf 1182 2014 Jacob Eisenstein nlp GitHub
974 Language Models https://github.com/shichao116/NLP/blob/master/slides.2014/lec6-slides.pdf 1015 2014 Jacob Eisenstein nlp GitHub
975 Dependency parsing https://github.com/shichao116/NLP/blob/master/slides.2014/lec14-depparsing-slides.pdf 1282 2014 Jacob Eisenstein nlp GitHub
976 WSD Key Concepts Word https://github.com/shichao116/NLP/blob/master/slides.2014/lec4-slides.pdf 1030 2014 Jacob Eisenstein nlp GitHub
977 Administrative corrections https://github.com/shichao116/NLP/blob/master/slides.2014/lec2-slides.pdf 1580 2014 Jacob Eisenstein nlp GitHub
978 Classication key concepts https://github.com/shichao116/NLP/blob/master/slides.2014/lec5-slides.pdf 1037 2014 Jacob Eisenstein nlp GitHub
979 Semi-Supervised Learning 1 https://github.com/shichao116/NLP/blob/master/slides.2014/ssl-slides.pdf 1203 2014 Jacob Eisenstein nlp GitHub
980 Information Extraction https://github.com/shichao116/NLP/blob/master/slides.2014/ie-slides.pdf 1089 2014 Jacob Eisenstein nlp GitHub
981 Anaphora and coreference https://github.com/shichao116/NLP/blob/master/slides.2014/coref-slides.pdf 1065 2014 Jacob Eisenstein nlp GitHub
982 Beyond Context-Free Grammars https://github.com/shichao116/NLP/blob/master/slides.2014/beyond-cfg.pdf 1078 2014 Jacob Eisenstein nlp GitHub
983 Discourse Structure https://github.com/shichao116/NLP/blob/master/slides.2014/discourse-slides.pdf 1138 2014 Jacob Eisenstein nlp GitHub
984 Natural Language Semantics https://github.com/shichao116/NLP/blob/master/slides.2014/formal-semantics-slides.pdf 1120 2014 Jacob Eisenstein nlp GitHub
985 Finite-State Transducers https://github.com/shichao116/NLP/blob/master/slides.2014/lec8-slides.pdf 1076 2014 Jacob Eisenstein nlp GitHub
986 Context Free Grammars and Parsing https://github.com/shichao116/NLP/blob/master/slides.2014/parsing-slides.pdf 1093 2014 Jacob Eisenstein nlp GitHub
987 Examples of Viterbi, Forward, and Backward https://github.com/shichao116/NLP/blob/master/slides.2014/lec9-hmm-slides.pdf 1182 2014 Jacob Eisenstein nlp GitHub
988 Modern statistical parsers https://github.com/shichao116/NLP/blob/master/slides.2014/modern-cfg-parsing.pdf 1099 2014 Jacob Eisenstein nlp GitHub
989 Natural Language Processing: A Speech Example https://people.eecs.berkeley.edu/~klein/cs288/fa14/slides/fa14lecture2-6pp.pdf 1049 2014 Dan Klein nlp UC Berkeley
990 Natural Language Processing: Classification I https://people.eecs.berkeley.edu/~klein/cs288/fa14/slides/fa14lecture13-1pp.pdf 1153 2014 Dan Klein nlp UC Berkeley
991 Natural Language Processing: Machine Translation III https://people.eecs.berkeley.edu/~klein/cs288/fa14/slides/fa14lecture19-1pp.pdf 1197 2014 Dan Klein nlp UC Berkeley
992 Natural Language Processing: Language Modeling II https://people.eecs.berkeley.edu/~klein/cs288/fa14/slides/fa14lecture3-6pp.pdf 1015 2014 Dan Klein nlp UC Berkeley
993 Natural Language Processing: Language Modeling III https://people.eecs.berkeley.edu/~klein/cs288/fa14/slides/fa14lecture4-1pp.pdf 1015 2014 Dan Klein nlp UC Berkeley
994 Natural Language Processing: Speech Inference https://people.eecs.berkeley.edu/~klein/cs288/fa14/slides/fa14lecture7-1pp.pdf 1539 2014 Dan Klein nlp UC Berkeley
995 Natural Language Processing: Tasks https://people.eecs.berkeley.edu/~klein/cs288/fa14/slides/fa14lecture21-1pp.pdf 1071 2014 Dan Klein nlp UC Berkeley
996 Natural Language Processing: The Speech Signal https://people.eecs.berkeley.edu/~klein/cs288/fa14/slides/fa14lecture5-1pp.pdf 1539 2014 Dan Klein nlp UC Berkeley
997 Natural Language Processing: Course Information https://people.eecs.berkeley.edu/~klein/cs288/fa14/slides/fa14lecture1-6pp.pdf 1580 2014 Dan Klein nlp UC Berkeley
998 Natural Language Processing: Parsing https://people.eecs.berkeley.edu/~klein/cs288/fa14/slides/fa14lecture10-1pp.pdf 1019 2014 Dan Klein nlp UC Berkeley
999 Grounded Semantics https://people.eecs.berkeley.edu/~klein/cs288/fa14/slides/fa14lecture22-1pp.pdf 1024 2014 Dan Klein nlp UC Berkeley
1000 Natural Language Processing Classification II https://people.eecs.berkeley.edu/~klein/cs288/fa14/slides/fa14lecture14-1pp.pdf 1157 2014 Dan Klein nlp UC Berkeley