INF8245AE - Machine Learning (Automne 2024)

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.

INF8250AE - Reinforcement Learning (Automne 2024)

Introduction to Reinforcement Learning. Multi-armed bandits. Contextual Bandits. Finite Markov Decision Process. Dynamic Programming. Policy Iteration. Value Iteration. Monte Carlo Methods. Temporal Difference Learning. n-step bootstrapping. On-policy prediction with function approximation. on-policy control with function approximation. off-policy control with function approximation. Policy Gradient Methods. REINFORCE. Actor-Critic. Determistic Policy Gradients. Natural Policy Gradient. TRPO and PPO. Model-based RL. Planning. Eligibility Traces. Hierarchical RL. POMDPs. inverse-RL. Exploration in RL. Off-line RL. Multi-agent RL.