University of California at Berkeley
Dept. of Electrical Engineering & Computer Science
Dept. of Statistics

EECS 281a / STAT 241a
Statistical Learning Theory --- Graphical Models

Fall Semester 2012

Practical information

Lectures: Tues/Thurs 14:00--15:30, LeConte Hall 2.

Recitations (optional): Wednesday 09:00--10:30, 306 Soda Hall.

Course reader: An Introduction to Probabilistic Graphical Models, by M. Jordan. Available at Copy Central, 44 Shattuck Square, starting 8/28.

Grading: Homework (60%) and Course Projects (40%), OR Homework (60%), Course Project (20%) and Exam (20%)


Martin Wainwright
Office Hours: Tues, Thurs 3:30--4:30, 263 Cory
Email: wainwrig AT eecs DOT berkeley DOT edu
Phone: 643-1978
Office: 263 Cory Hall

Graduate student instructors:

Andre Wibisono
Office Hours: Monday 4--5 pm, 411 Soda Hall
Email: wibisono AT eecs DOT berkeley DOT edu

Hongwei Li
Office Hours: Friday 2--3 pm, 307 Evans Hall
Email: hwli AT stat DOT berkeley DOT edu

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Course description: This course is a 3-unit course that provides an introduction to the area of probabilistic models based on graphs. These graphical models provide a very flexible and powerful framework for capturing statistical dependencies in complex, multivariate data. Key issues to be addressed include representation, efficient algorithms, inference, and statistical estimation. These concepts will be illustrated using examples drawn from various application domains, including machine learning, signal processing, communication theory, computational biology, computer vision, etc.

Outline: Required background: The prerequisites are previous coursework in linear algebra, multivariate calculus, basic probability and statistics (at the level of EE 126). Some degree of mathematical maturity is also required. Coursework or background in graph theory, information theory, optimization theory, and statistical physics is relevant, and could be helpful but is not required. Familiarity with a matrix-oriented programming language (e.g., MATLAB, R, Splus, etc.) will be necessary.

Homework: Although it is acceptable for students to discuss the homework assignments with one another, each student must write up his/her homework on an individual basis. Each student must indicate with whom (if anyone) they discussed the homework problems. Homeworks must be turned in at the beginning of class on the due date. Late homeworks will not be accepted. We will not accept electronic submissions.

Course project: The course project will involve independent work on a topic of the student's own choosing. Course projects will be presented in an informal poster session at the end of semester, and the work will be summarized in a write-up.

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Updates and Announcements

  • First class will be held on Tuesday, August 28.

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  • Wed, Dec 12: Homework #7 Solutions .
  • Sat, Dec 8: Homework #6 Solutions .
  • Tues, Nov 20: Wainwright and Jordan paper on variational methods. Chapter 5 is on mean field; see in particular Example 5.2 (p. 134) for details on naive mean field on Ising model.
  • Sun, Nov 18: Posted some reading material about neighborhood-based graph selection
    Logistic regression and Ising models: Paper
  • Tues, Nov 13: Homework #7 due Thurs Nov 29.
  • Mon, Nov 5: Homework #5 Solutions .
  • Thurs, Nov 1: Homework #4 Solutions .
  • Tue, Oct 30: Homework #6, due Tuesday, November 13. Data files: Y.dat, Lambda.dat, Ymodel.dat, Xmodel.dat, Ynew.dat, Xnew.dat.
  • Thu, Oct 25: Homework #3 solution.
  • Wed, Oct 17: Information sheet on course projects. Poster presentations will be given on Monday, December 10 from 3--5pm in the Wozniak Lounge, Soda Hall.
  • Tue, Oct 16: Homework #5, due Tuesday, October 30. Data files: hmm-gauss.dat, hmm-test.dat, Pairwise.dat.
  • Thu, Oct 4: Homework #2 solution.
  • Tue, Oct 2: Homework #4, due Tuesday, October 16.
  • Tue, Sep 18: Homework #3, due Tuesday, October 2.
  • Thu, Sep 13: Homework #1 solution.
  • Tue, Sep 4: Homework #2, due on Thursday, September 13. Here are the auxiliary files: lms.dat, classification2d.dat, and testing.dat. Please note that you must turn in a paper copy of your homework in class. Also, as per course policy, it is not possible to consider late homeworks. Corrections: In 2.5(a), "binomial entropy" should be "Bernoulli entropy". In 2.6(a), \phi_1 should be T_1.
  • Fri, Aug 31: Chapter 6 and chapter 8 from the reader.
  • Wed, Aug 29: Slides from the first recitation (review on probability, statistics, and linear algebra).
  • Tues, Aug 28: Homework #1, due on Tuesday, September 4. Homework #1 is purely on undergraduate review material; if you are not familiar with it, then you do not have the appropriate background for this course, and will likely not benefit from taking it.
  • Tues, Aug 28: Syllabus
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    Supplementary reading

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