Instructor: Ng, Andrew

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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
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

Download complete set of course materials. (Includes all available handouts, assignments, exams, and computer software. Does not include video assets)