Lecture 1: Introduction

Lecture 2: Linear dimensionality reduction

Lecture 3: Principal component analysis (PCA)

Lecture 4: Linear prediction, maximum likelihood, regularization and cross-validation

Lecture 5: Probability review and intro to Bayes

Lecture 6: More on Bayes and regression

Lecture 7: Optimization and logistic regression

Lecture 8: Neural networks

Lecture 9: The Monte Carlo Method

Lecture 10: Information, computation, Energy, Dynamical systems and Boltzmann machines

Lecture 11: The Mathematics of restricted Boltzmann machines

Lecture 12: Clustering and Mixture models: K-means and the EM algorithm

Lecture 13: Gaussian processes, active learning, bandits and Bayesian optimization

Lecture 14: Ensemble methods: Boosting and random forests

Lecture 15: Bayesian networks, factored graphs, and conditional random fields

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