Computer Science 294

Practical Machine Learning

(Fall 2009)

jordan-AT-cs

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]

Dec 1 : Prof. Jordan's office hours will be on Friday from 4:00 to 5:00 this week.

: 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.

Course prerequisites : some prior exposure to probability and to linear algebra.

: 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.

: 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'.

Homework 1: [hw1.pdf]. Additional files for homework 1: [hw1-files.zip]. Direct questions on the classification questions to Mike Jordan (jordan@eecs) and on the regression questions to Fabian Wauthier (flw@eecs).

Homework 2: [hw2.pdf]. Additional files for homework 2: [hw2-data.zip]. Direct questions on the clustering questions to Sriram Sankararaman (sriram_s@eecs) and on the dimensionality reduction questions to Percy Liang (pliang@eecs).

Homework 3: [hw3.pdf]. Additional files for homework 3: [hw3.zip]. Direct questions on the feature selection questions to Alex Bouchard (bouchard@eecs) and on the HMM questions to Alex Simma (asimma@eecs).

Homework 4: [hw4.pdf]. Additional files for homework 4: [hw4.zip]. Direct questions on the collaborative filtering questions to Lester Mackey (lmackey@eecs) and on the active learning question to Daniel Ting (dting@stat).

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. Present a poster about your project. Submit your project write-up on bSpace.

Hastie, Tibshirani and Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction . Book's web site

. Book's web site Witten and Frank. Data Mining: Practical Machine Learning Tools and Techniques . Book's web site

. 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.