Machine 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 le... (read more)

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. (read less)

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 le... (read more)

Video Lectures

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