Machine Learning Course at Stanford
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CS 229 |
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Handouts |
- Handout #1: Course Information (HTML)
- Handout #2: Tentative Course Schedule (HTML)
- Handout #3: Project Guidelines (ps) (pdf) (corrected due times, 10/16)
- Handout #4: Problem Set 1 (ps) (pdf) (data)
- Handout #5: Problem Set 2 (ps) (pdf) (starter code)
- Handout #6: The Simplified SMO Algorithm (ps) (pdf) (corrected error: 10/22)
- Reference article: J. Platt, Fast Training of Support Vector Machines using Sequential Minimal Optimization (ps.gz) (pdf)
- Handout #7: Problem Set 1 Solutions (ps) (pdf)
- Handout #8: Problem Set 3 (ps) (pdf) (images)
- Handout #9: Anonymous midterm survey (ps) (pdf)
- Handout #10: Practice Midterm (ps) (pdf)
- Handout #11: Practice Midterm Solutions (ps) (pdf) (updated, 11/05)
- Handout #12: Problem Set 2 Solutions (ps) (pdf)
- Handout #13: Problem Set 4 (ps) (pdf) code for q4 code for q6 (corrected Octave bug, 11/26)
- Note: this is due on Monday, December 1
- Handout #14: Midterm Solutions (ps) (pdf)
- Handout #15: Problem Set 3 Solutions (ps) (pdf)
- Handout #16: List of related AI classes (ps) (pdf)
- Handout #17: Machine Learning: Other readings (ps) (pdf)
- Handout #18: Problem Set 4 Solutions (ps) (pdf)
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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
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- Section notes 1 (ps) (pdf) Linear Algebra Review and Reference (updated, 10/7)
- Section notes 2 (ps) (pdf) Probability Theory Review
- Section notes 3a (ps) (pdf) Multivariate Gaussians
- Section notes 3b Matlab Review
- Section notes 4 (ps) (pdf) Convex Optimization Overview (part I) (matlab demo)
- Section notes 5 (ps) (pdf) Convex Optimization Overview (part II)
- Section notes 6 (ps) (pdf) Midterm review
- Section notes 7 (ps) (pdf) More on Gaussians
- Section notes 8 (ps) (pdf) Gaussian Processes (matlab demo)
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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://homepages.nyu.edu/~kpl2/dsts6/octaveTutorial.html and http://wiki.aims.ac.za/mediawiki/index.php/Octave.
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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Comments to cs229-qa@cs.stanford.edu |