Computer Science 294
Practical Machine Learning
(Fall 2009)

Prof. Michael Jordan (jordan-AT-cs)

Lecture: Thursday 5-7pm, Soda 306
Office hours of the lecturer of the week: Mon, 3-4 (751 Soda); Weds, 2-3 (751 Soda)
Office hours of Prof. Jordan: Weds, 3-4 (429 Evans)

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]



Lectures (Tentative Schedule)


There will be bi-weekly homeworks, worth a total of 60% of your grade. Each homework is due at the beginning of class. Please keep your responses succinct and clear. There is no need to attach code. Turn in your homework on bSpace (click Assignments on the left menu).

  • Homework 1: [hw1.pdf]. Additional files for homework 1: []. 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: []. 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: []. 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: []. Direct questions on the collaborative filtering questions to Lester Mackey (lmackey@eecs) and on the active learning question to Daniel Ting (dting@stat).
  • Project

    The project counts for roughly 40% of your grade. The general idea for the project is to have you apply a concept from the class in your own research, or explore it further through experimentation. The evaluation of the project will be based on the following three deliverables:
    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

    Readings for the specific sections will be provided in the future. There are several good resources which contain general information.


    There is a wide variety of free data mining and machine learning software available. You might find them useful for doing the homeworks or the final project.
    Last updated Aug. 22, 2009.