Machine Learning Topics Introduction (1 class) Basic concepts. Learning theory. (4 classes) Bias/variance tradeoff. Union and Chernoff/Hoeffding bounds. VC dimension. Worst case (online) learning. Practical advice on how to use learning algorithms. Supervised learning. (8 classes) Supervised learning setup. LMS. Logistic regression. Perceptron. Exponential family. Kernel methods: Radial Basis Networks, Gaussian Processes, and Support Vector Machines. Model selection and feature selection. Ensemble methods: Bagging, boosting. Evaluating and debugging learning algorithms. Reinforcement learning and control. (6 classes) MDPs. Bellman equations. Value iteration and policy iteration. TD, SARSA, Q-learning. Value function approximation. Policy search. Reinforce. POMDPs. Multi-Armed Bandit.