Part I: WHAT WE APPLY

1. Research papers
1.1 Machine learning algorithms

Support vector regression
Clustering and Support Vector Regression for Water Demand Forecasting and Anomaly Detection

Canonical Correlation Analysis (CCA)
Multi Channel-Kernel Canonical Correlation Analysis for Cross-View Person Re-Identification

K-nearest neighbour classification (K-NN)
Detecting impact factor manipulation with data mining techniques

Decision trees
InfiniteBoost: building infinite ensembles with gradient descent (2017)
Stochastic Gradient Boosted Distributed Decision Trees

Self Organized maps
A Visual Measure of Changes to Weighted Self-Organizing Map Patterns
A self-organizing map analysis of survey-based agents' expectations before impending shocks for model selection: The case of the 2008 financial crisis
AMSOM: Adaptive Moving Self-organizing Map for Clustering and Visualization

Multi-armed Bandit model
Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems
Analysis of Thompson Sampling for the Multi-armed Bandit Problem

Boltzmann Machines
A Practical Guide to Training Restricted Boltzmann Machines

Deep Learning
Dropout: A Simple Way to Prevent Neural Networks from Overfitting

1.2 Time Series analysis/Potfolio trading algorithms
Autoregressive Convolutional Neural Networks for Asynchronous Time Series
Grouped Convolutional Neural Networks for Multivariate Time Series
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces
Deep Learning for Time-Series Analysis
Deep Learning for Time Series Modeling(2012)
Forecasting Markets using eXtreme Gradient Boosting (XGBoost)
A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction (2017)
Stock Trading Using PE ratio: A Dynamic Bayesian Network Modeling on Behavioral Finance and Fundamental Investment
Forecasting of preprocessed daily solar radiation time series using neural networks
1.3 Dimensionality reduction
Dimensionality Reduction using Similarity-induced Embeddings 2017

1.4 Other algorithms
Food-bridging: a new network construction to unveil the principles of cooking
Distilling the Knowledge in a Neural Network

Books
Machine Learning. A Probabilistic Perspective. Kevin P. Murphy

Articles
5 algorithms to train a neural network
Notes on Artificial Intelligence
A new kind of deep neural networks

Cheat Sheets
NumPy Cheat Sheet - Python for Data Science

Visualisations and presentations
"Seeing theory"
"Big Data landscape 2016"
Linear Regression and Support Vector Regression

Part II: HOW TO APPLY
1. Software and applications libraries

1.1 Platforms

Sonnet released opensource
TensorFlow Board overview for data scientists

1.2 Specific libraries
1.2.1 Time series analysis code
1.2.2 Computer vision
1.2.3 Speech to text
1.2.4. More methods
Machine Learning: Regression of 911 Calls