INF Archives

Introduction - Prediction - Statistical Decision Theory - Linear Regression - Non-linear Regression - Bias-variance tradeoff - Linear Classification - Indicator Regression - PCA - LDA - QDA - GDA - Naive Bayes - Logistic Regression - Perceptron - Separating Hyperplanes - SVM - Decision Trees - ensemble learning - bagging - boosting - stacking - Neural Networks - Backpropagation - Training Deep Neural Nets - Optimization Methods - Convnets - RNNs - Estimation Theory - Maximum Likelihood Estimation - Maximum A Posteriori Estimation - Bayesian Learning - Bayesian Linear Regression - Kernel Methods - Gaussian Process - Computational Learning Theory - Frontiers in ML.

Introduction to Reinforcement Learning - Multi-armed bandits - Policy Gradient Methods - Contextual Bandits - Finite Markov Decision Process - Dynamic Programming - Policy Iteration - Value Iteration - Monte Carlo Methods - Temporal Difference Learning - n-step bootstrapping - Eligibility Traces - Model-based RL - Planning - On-policy prediction with function approximation - on-policy control with function approximation - off-policy control with function approximation - Hierarchical RL - POMDPs - inverse-RL - Exploration in RL - Offline RL.