This course introduces core statistical machine learning algorithms in a (relatively) non-mathematical way, emphasizing applied problem-solving. The prerequisites are light; some prior exposure to basic probability and to linear algebra will suffice. A list of topics can be found here.

[Announcements] [Administrivia] [Lectures] [Homework] [Project] [Readings] [Software]

Announcements

• Dec 1: Prof. Jordan's office hours will be on Friday from 4:00 to 5:00 this week.
• Nov 19: Project writeups will be due on Tues, December 15th at 5:00 pm (on bSpace). The poster session will be on Thurs, December 17th at 5:00 in 306 Soda.

Administrivia

• Course prerequisites: some prior exposure to probability and to linear algebra.
• Coursework and grading: Students will be required to complete bi-weekly homework assignments. These must be turned in on time to receive credit. There will also be a final project. A project report will be required and projects will also be presented in an end-of-term poster session. The homeworks will count for 60% of the grade and the project will count for 40% of the grade.
• bSpace: use the forum group there to discuss homeworks, project topics, ask questions about the class, etc. To access bSpace, simply visit https://bspace.berkeley.edu and login using your CalNet ID. If you don't have a CalNet ID, send an email to jordan-AT-cs to request a guest account. If you're not registered to the class or the tab for the course doesn't show up, you can add it by going through My Workspace | Membership, then click on 'Joinable Sites' and search for 'COMPSCI 294 LEC 034 Fa09'.

Lectures (Tentative Schedule)

• Aug 27: Tutorial [Ariel Kleiner]
• Sep 3: Classification [Michael Jordan]
• Sep 10: Regression [Fabian Wauthier]
• Sep 17: Clustering [Sriram Sankararaman]
• Sep 24: Dimensionality reduction [Percy Liang]
• Oct 1: Feature selection [Alex Bouchard]
• Oct 8: Hidden Markov models, graphical models [Alex Simma]
• Oct 15: Collaborative Filtering [Lester Mackey]
• Oct 22: Active learning, experimental design [Daniel Ting]
• Oct 29: Reinforcement learning [Peter Bodik]
• Nov 5: Bootstrap, cross-validation, ROC plots [Michael Jordan]
• Nov 12: Time series, sequential hypothesis testing, anomaly detection [Alex Shyr]
• Nov 19: Bayesian nonparametric methods (Dirichlet processes) [Kurt Miller]
• Dec 3: Optimization methods for learning [John Duchi]

Homework

Project

1. Submit on bSpace one paragraph describing your project plan or ideas by Thursday, November 5. The idea is to have you start working on the project before December... Feel free to come to OH to discuss project ideas, to send emails to the lecturers, or to use the wiki/discussion group on bSpace to brainstorm ideas.
2. Present a poster about your project.
3. Submit your project write-up on bSpace.

Suggested Reading

• Hastie, Tibshirani and Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Book's web site
• Witten and Frank. Data Mining: Practical Machine Learning Tools and Techniques. Book's web site
• Andrew Moore's Tutorials are a collection of PDF tutorials on many of the topics that will be covered in the class.

Software

• Weka is a large Java package implementing many learning algorithms.
• RapidMiner (formerly known as YALE) is an alternative (and complementary) Java package. It includes a GUI which allows automation of the whole data path from feature normalization through feature selection, learning and cross validation.
• SVM-Light and LibSVM are two popular implementations of various SVM algorithms
• R is an interactive programming language designed for statistics. Many very useful libraries are available.