CS 731: Advanced Artificial Intelligence ("Machine Learning 2")
Spring 2011

Homeworks: Follow this link for assignments and policies Exam solution Topics: Schedule: 9:30--10:45 MWF. 103 Psychology This class is "Machine Learning 2", the second installment of the machine learning sequence following CS760. The goal is to further prepare you as a machine learning researcher. Here is an analogy: In CS760, we opened up a toolbox for you, and you learned how to use hammers, screw drivers, wrenches, etc. (translation: SVMs, naive Bayes, decision trees, etc.) for your home improvement projects. In this class, we will do three things: 1) Show you some more powerful tools. But if your interest is only in using machine learning tools, this class is not for you; 2) Teach you the equivalent of mechanical engineering so you can invent new tools; 3) Teach you the equivalent of physics so you understand why the tools work. We will cover both foundations and cutting-edge topics in statistical machine learning. This will be a more theoretical, less practical class. Prerequisites: Officially CS540. Taking this class after CS760 or equivalent is recommended but not strictly required. Previous coursework in linear algebra, multivariate calculus, basic probability and statistics is required. Familiarity with a matrix-oriented programming language (e.g., MATLAB, R, S-plus etc.), and math maturity at this level is recommended. Homeworks to be typeset with Latex. If you have taken CS731 before but would like to take this new version of the class for credit, please send me an email -- we will address it on a case-by-case basis. Instructor: Xiaojin (Jerry) Zhu Office: 6391 CS E-mail: jerryzhu@cs.wisc.edu Phone: 608-890-0129 Office Hours: 3:45-4:45pm Tuesdays, or by appointment Teaching Assistant: There will be none. Instead, let us do crowd sourcing and help ourselves. Please use the class mailing list (see below) to ask any questions, from Latex, to Matlab, to specific class content. Please also be a good citizen and help answer your fellow students' questions. The frequency and quality of answers will be factored into consideration in your course grade. Textbook: By taking this course, you are serious about machine learning. We will draw materials from multiple sources and there will not be a single required textbook. Nonetheless, you should obtain these books. They are excellent reference books for you down the road and worth more than gold of equal weight. [1] (AoS) Larry Wasserman, All of Statistics: A Concise Course in Statistical Inference. Springer, 2003. [2] (PRML) Christopher M. Bishop, Pattern Recognition and Machine Learning. Springer Verlag, 2006. [3] (ESL) Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Second Edition, 2009. (Available online) Grading: Homeworks (30%), midterm exam (40%), and a project (30%). Other: Class mailing list: compsci731-1-s11@lists.wisc.edu (archive) Course URL: http://pages.cs.wisc.edu/~jerryzhu/cs731.html