Although the lecture videos and lecture notes from Andrew Ng‘s Coursera MOOC are sufficient for the online version of the course, if you’re interested in more mathematical stuff or want to be challenged further, you can go through the following notes and problem sets from CS 229, a 10-week course that he teaches at Stanford (which also happens to be the most enrolled course on campus). It’s not hard to end up with a 100% score on his MOOC which is obviously a (much) watered down version of the course he teaches at Stanford, at least in terms of difficulty. If you don’t believe me, just have a go at the problem sets from the links below.

Lecture Notes

• Lecture notes 1 (ps) (pdf)   Supervised Learning, Discriminative Algorithms
• Lecture notes 2 (ps) (pdf)   Generative Algorithms
• Lecture notes 3 (ps) (pdf)   Support Vector Machines
• Lecture notes 4 (ps) (pdf)   Learning Theory
• Lecture notes 5 (ps) (pdf)   Regularization and Model Selection
• Lecture notes 6 (ps) (pdf)   Online Learning and the Perceptron Algorithm. (optional reading)
• Lecture notes 7a (ps) (pdf)   Unsupervised Learning, k-means clustering.
• Lecture notes 7b (ps) (pdf)   Mixture of Gaussians
• Lecture notes 8 (ps) (pdf)   The EM Algorithm
• Lecture notes 9 (ps) (pdf)   Factor Analysis
• Lecture notes 10 (ps) (pdf)   Principal Components Analysis
• Lecture notes 11 (ps) (pdf)   Independent Components Analysis
• Lecture notes 12 (ps) (pdf)   Reinforcement Learning and Control

Section Notes

• Section notes 1 (pdf)   Linear Algebra Review and Reference
• Section notes 2 (pdf)   Probability Theory Review
• Files for the Matlab tutorial: sigmoid.m, logistic_grad_ascent.m, matlab_session.m
• Section notes 4 (ps) (pdf)   Convex Optimization Overview, Part I
• Section notes 5 (ps) (pdf)   Convex Optimization Overview, Part II
• Section notes 6 (ps) (pdf)   Hidden Markov Models
• Section notes 7 (pdf)   The Multivariate Gaussian Distribution
• Section notes 8 (pdf)   More on Gaussian Distribution
• Section notes 9 (pdf)   Gaussian Processes

Handouts and Problem Sets

• Handout #1: Course Information (HTML) (pdf)
• Handout #2: Course Schedule (HTML) (pdf)
• Handout #3: Cover Sheet
• Handout #4: Practice Midterm 1 Solution: Solution
• Handout #5: Practice Midterm 2 Solution: Solution
• Problem Set 1 (pdf) Data: q1x.dat, q1y.dat, q2x.dat, q2y.dat Solution: Solution (pdf)
• Problem Set 2 (pdf) Data: ps2.zip Solution: Solution (pdf)
• Problem Set 3 (pdf) Solution: Solution (pdf)
• Problem Set 4 (pdf)