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Note: Bandits notes is highly incomplete.

Introduction to Stochastic Optimization

• Introduction to zeroth-order optimization

• Machine learning applications

• Smoothness, convexity and strong convexity

• Martingales

• Introduction to stochastic approximation

• Asymptotic convergence of stochastic approximation

• Variants of zeroth-order gradient estimates

• Bounds for gradient descent (noiseless case)

• Non-asymptotics for stochastic gradient

• Reinforcement learning

Introductory Lectures on Machine Learning

• Probability review

• Singular value decomposition, principal component analysis

• Bayes classifier, maximum likelihood estimation

• Linear models

• PAC learning

• Crash course on optimization

• Logistic regression

• Support vector machines, kernel methods

• Online learning

• Mixture models

Markov Chains

• Poisson processes

• DTMCs (transient behavior)

• DTMCs (limiting behavior)

• CTMCs

Lectures on Reinforcement Learning Theory

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• Finite horizon MDPs

• Stochastic shortest path problems

• Discounted MDPs

• RL foundations

• Full state algorithms

• Markov chains (review)

• RL with function approximation

• Policy gradient algorithms