Course Handouts

Handout	Description
info.pdf	Course Information
schedule.pdf	Course Schedule
AI-classes.pdf	Other AI Courses

Lecture Handouts

Handout	Description
cs229-notes1.pdf	Linear Regression, Classification and logistic regression, Generalized Linear Models
cs229-notes2.pdf	Generative Learning algorithms
cs229-notes3.pdf	Support Vector Machines
cs229-notes4.pdf	Learning Theory
cs229-notes5.pdf	Regularization and model selection
cs229-notes6.pdf	The perceptron and large margin classifiers
cs229-notes7a.pdf	The k-means clustering algorithm
cs229-notes7b.pdf	Mixtures of Gaussians and the EM algorithm
cs229-notes8.pdf	The EM algorithm
cs229-notes9.pdf	Factor analysis
cs229-notes10.pdf	Principal components analysis
cs229-notes11.pdf	Independent Components Analysis
cs229-notes12.pdf	Reinforcement Learning and Control

Review Notes

Topic	Handouts
Linear Algebra Review and Reference	cs229-linalg.pdf
Probability Theory Review	cs229-prob.pdf
Matlab Review	logistic_grad_ascent.txt sigmoid.txt matlab_session.txt
Convex Optimization Overview, Part I	cs229-cvxopt.pdf
Convex Optimization Overview, Part II	cs229-cvxopt2.pdf
Hidden Markov Models	cs229-hmm.pdf
Gaussian Processes	cs229-gp.pdf compute_kernel_matrix.txt gp_demo.txt sample_gp_prior.txt