Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng delves into learning theory, covering bias, variance, empirical risk minimization, union bound and Hoeffding's inequalities.

1. Introduction: Applications of Machine Learning, Logistics of the Class, Learning Theory
2. Linear Regression, Gradient Descent, Normal Equations
3. Locally Weighted Regression, Probabilistic Interpretation, Logistic Regression
4. Newton's Method, Exponential Family, Generalized Linear Models (GLMs)
5. Learning Algorithms: Generative, Gaussian Discriminant Analysis, Digression
6. Neural networks, Naive Bayes, Support Vector Machines
7. Optimal Margin Classifier, Primal/Dual Optimization (KKT), SVM Dual, Kernels
8. Support Vector Machines: Kernels, Soft Margin, SMO Algorithm
9. Learning Theory I: Bias/Variance, ERM, Union Bound, Hoeffding Inequality, Uniform Convergence
10. Learning Theory II: Uniform Convergence, VC Dimension, Model Selection
11. Bayesian Statistics and Regularization, Online Learning, Machine Learning Algorithms
12. Unsupervised Learning, Clustering, Mixtures of Gaussians, Jensen's Inequality, EM Algorithm
13. Expectation Maximization: Mixture of Gaussians, Naive Bayes, Factor Analysis
14. The Factor Analysis EM steps, Principal Component Analysis (PCA)
15. PCA: Latent Semantic Indexing, Singular Value Decomposition, Independent Component Analysis
16. Reinforcement Learning I: MDP, Value Function, Value Interation, Policy Iteration
17. Reinforcement Learning II: Continuous State MDP, Discretization, Models/Simulators, Fitted Value
18. State-action Rewards, Finite Horizon MDPs, Dynamical Systems: Models, LQR, Riccati Equation
19. Debugging RL Algorithm, Linear Quadratic Regulation, Kalmer Filters, Linear Quadratic Gaussian
20. POMDPs, Pegasus Algorithm, Pegasus Policy Search, Applications of Reinforcement Learning

This course (CS229) -- taught by Professor Andrew Ng -- provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.