CS 229: Machine Learning (Course handouts)

CS 229 Machine Learning Course Materials

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Handouts and Problem Sets

 

• Handout #1: Course Information (HTML)
• Handout #2: Course Schedule (HTML)
• Handout #3: Final Project Guidelines (PDF)
  
• Problem Set 1 (pdf) Data: q1x.dat, q1y.dat, q2x.dat, q2y.dat PS1 Solution (pdf)
• Problem Set 2 (pdf) Data: ps2.zip PS2 Solution (pdf)
• Problem Set 3 (pdf) Data: mandrill-small.tiff, mandrill-large.tiff PS3 Solution (pdf)
• Problem Set 4 (pdf) Data: q4.zip, q6.zip PS4 Solution (pdf)
• Practice Midterm (pdf) Solutions

 

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

 

Other resources

Advice on applying machine learning: Slides from Andrew's lecture on getting machine learning algorithms to work in practice can be found here.

Previous projects: A list of last year's final projects can be found here.

Matlab resources: Here are a couple of Matlab tutorials that you might find helpful: http://www.math.ufl.edu/help/matlab-tutorial/ and http://www.math.mtu.edu/~msgocken/intro/node1.html. For emacs users only: If you plan to run Matlab in emacs, here are matlab.el, and a helpful .emac's file.

Octave resources: For a free alternative to Matlab, check out GNU Octave. The official documentation is available here. Some useful tutorials on Octave include http://en.wikibooks.org/wiki/Octave_Programming_Tutorial and http://www-mdp.eng.cam.ac.uk/web/CD/engapps/octave/octavetut.pdf .

Data: Here is the UCI Machine learning repository, which contains a large collection of standard datasets for testing learning algorithms. If you want to see examples of recent work in machine learning, start by taking a look at the conferences NIPS (all old NIPS papers are online) and ICML. Some other related conferences include UAI, AAAI, IJCAI.

Viewing PostScript and PDF files: Depending on the computer you are using, you may be able to download a PostScript viewer or PDF viewer for it if you don't already have one.

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