Cybernetics

• 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)

• 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 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)

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.