20 lectures of around 1 hour 15 each by Professor Andrew Ng. Contents: - Linear regression, gradient descent and normal equations (Lecture 2) - Locally weighted regression, probabilistic interpretation and logistic regression (Lecture 3) - Newton's method, exponential families, and generalized linear models (Lecture 4) - Generative learning algorithms and Gaussian discriminative analysis (Lecture 5) - Naive Bayes, neural networks, and support vector machine (Lecture 6) - Optimal margin classifiers, KKT conditions, and SUM duals (Lecture 7) - Support vector machines, including soft margin optimization and kernels (Lecture 8) - Learning theory, covering bias, variance, empirical risk minimization (Lecture 9) - Learning theory by discussing VC dimension and model selection (Lecture 10) - Bayesian statistics, regularization, digression-online learning (Lecture 11) - Unsupervised learning in the context of clustering, Jensen's inequality (Lecture 12) - Expectation-maximization (Lecture 13) - Factor analysis and expectation-maximization steps (Lecture 14) - Principal component analysis (PCA) and independent component analysis (Lecture 15) - Reinforcement learning, focusing particularly on MDPs, value functions (Lecture 16) - Reinforcement learning, focusing particularly on continuous state MDPs (Lecture 17) - State action rewards, linear dynamical systems (Lecture 18) - Debugging process, linear quadratic regulation, Kalmer filters (Lecture 19) - POMDPs, policy search, and Pegasus in the context of reinforcement learning. (Lecture 20)

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