SUMMARY.md

Table of contents

• Tales of Science and Data

Meta & resources

• The meta on all this
• Beautiful web of data science

Probability, statistics and data analysis

• Probability, its interpretation, and statistics
• Foundational concepts on distribution and measures
  • Probability functions and displaying data
  • Moments of a distribution and summary statistics
  • (Some of) the most famous distributions
  • Independence, joint/marginal/conditional probability, covariance and correlation
• Hypothesis testing
  • On sampling and testing
  • Feeling confident on a test result?
  • Testing if datasets are different
  • The chi squared test
  • ANOVA: analysis of variance
• Methods, theorems & laws
  • Bayes' theorem and conjugate distributions
  • The law of large numbers
  • The central limit theorem
  • The maximum likelihood, maximum a posteriori and expectation-maximisation estimation methods
  • Propagation of uncertainty
  • The Monte Carlo method
• Notable brain teasers, paradoxes and how to be careful with data
  • The Monty Hall problem
  • Some statistical paradoxes
  • The Anscombe's quartet
  • Using aggregated metrics well

Machine Learning: concepts & procedures

• Overview of the field
  • A learning machine
  • Classifying data
• Learning algorithms
  • The gradient descent method
• Feature building and modelling techniques
  • Feature engineering techniques
  • Feature selection techniques
  • Regularisation techniques
• Dimensionality reduction and matrix factorisation
  • Principal component analysis
  • Non-negative matrix factorisation
  • Singular value decomposition

Machine Learning: fundamental algorithms

• Learning paradigms
• Supervised learning
  • Linear regression
  • Polynomial regression
  • Decision trees
  • Logistic regression
  • Naive Bayes
  • k-nearest neighbours
  • Random forest
  • Support vector machine
  • AdaBoost
  • Gradient boosting
• Unsupervised learning
  • k-means

Machine Learning: model assessment

• Generic problems models can have
  • Overfitting and underfitting
• Performance metrics and validation techniques
  • Training, validation and test sets
  • Classification metrics
  • Regression metrics
  • Cross-validation
• Diagnostics
  • What's in the residuals of a regression?
  • The ROC curve

Artificial neural networks

• Overview of neural networks
  • Artificial neural networks in a nutshell
  • The backpropagation mechanism
  • Setting up neural networks
• Types of neurons and networks
  • The Perceptron
  • The sigmoid neuron
  • Deep neural networks
  • Convolutional neural networks

Natural language processing

• General concepts & tasks in NLP
  • Some concepts and basic tasks in Linguistics
  • Text as numerical features
• Manipulating text and extracting information
  • Parsing, POS tagging and stemming text
  • Information extraction
• Topic Modelling
  • Intro
  • Latent semantic analysis
  • Probabilistic latent semantic analysis
  • Latent Dirichlet allocation
• Word Embeddings
  • word2vec
  • doc2vec

Computer vision

• Intro: quantifying images & some glossary
  • Notes on the formalism
• Processing an image
  • The intensity histogram
  • Blurring, inverting, thresholding an image
  • Morphological operations
• What's in an image
  • Edge detection and object contours
  • Face recognition
  • Object detection without ANNs
  • Object detection with ANNs

The computer science appendix

• What's this
• Notes on foundations
  • Computational models and algorithmic complexity
  • Computer architecture and programming languages
  • (Main) data structures
  • Algorithmic efficiency
• Essential algorithms
  • Sorting algorithms

The mathematics appendix

• Matrix algebra notes
• Mathematical functions
• Some geometry
• Cross-field concepts
• (Some) mathematical measures

Toolbox

• The Python data stack
• Databases and distributed frameworks
  • Apache Hadoop
  • Apache Spark
  • Elasticsearch
• Notebook tools