CS229 - Machine Learning

CS229: Machine Learning (Stanford Univ.). Taught by Professor Andrew Ng, this course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing. (from

An Overview of the Course

Lecture 01 - An Overview of the Course
Lecture 02 - Linear Regression, Gradient Descent, Normal Equations
Lecture 03 - Locally Weighted Regression, Probabilistic Interpretation, Logistic Regression
Lecture 04 - Newton's Method, Exponential Family, General Linear Models
Lecture 05 - Discriminative Algorithms, Generative Algorithms, Gaussian Discriminant Analysis
Lecture 06 - Neural Network, Applications of Neural Network, Support Vector Machine
Lecture 07 - Optimal Margin Classifier, Karush-Kuhn-Tucker Conditions, SVM Dual
Lecture 08 - Kernels, Mercer's Theorem, Soft Margin SVM, SMO Algorithm, Applications of SVM
Lecture 09 - Bias/variance Tradeoff, Empirical Risk Minimization, The Union Bound, Hoeffding Inequality
Lecture 10 - The Concept of 'Shatter' and VC Dimension, Model Selection, Feature Selection
Lecture 11 - Bayesian Statistics and Regularization, Online Learning, Applications of Machine Learning Algorithms
Lecture 12 - The Concept of Unsupervised Learning
Lecture 13 - Mixture of Gaussian, Mixture of Naive Bayes, Factor Analysis
Lecture 14 - The Factor Analysis Model, 0 EM for Factor Analysis, Principal Component Analysis
Lecture 15 - Latent Semantic Indexing, Independent Component Analysis (ICA), Applications of ICA
Lecture 16 - Applications of Reinforcement Learning, Markov Decision Process (MDP)
Lecture 17 - Generalization to Continuous States, Discretization, Fitted Value Iteration, Optimal Policy
Lecture 18 - Finite Horizon MDPs, The Concept of Dynamical Systems, Linear Quadratic Regulation
Lecture 19 - Debugging Process, Linear Quadratic Regularization, Kalman Filter, Linear Quadratic Gaussian
Lecture 20 - Partially Observable MDPs, Policy Search, Pegasus Algorithm

CS229 - Machine Learning
Instructors: Professor Andrew Ng. Handouts. Assignments. Exams. This course provides a broad introduction to machine learning and statistical pattern recognition.