CSE 254 Spring 2003

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
UNIVERSITY OF CALIFORNIA, SAN DIEGO

CSE 254: Seminar on Learning Algorithms

Spring 2003

Please ask general questions here, for example about Unix alternatives to Powerpoint. As an example of an excellent project proposal, here is the proposal written by Honghao Shan.

CSE 254 is a graduate seminar devoted to recent research on AI learning methods and applications. This is not an introductory course, so the prerequisite is at least one graduate-level course (at UCSD or elsewhere) in machine learning or a closely related area such as statistics or pattern recognition. Appropriate courses at UCSD include CSE 291 (Statistical Learning), CSE 253 (Neural Networks for Pattern Recognition), and Cognitive Science 260 (Pattern Recognition).

The room for CSE 254 is APM 4882. In order to have this room, the class meets on Mondays from 5pm to 6:20pm, and on Wednesdays from 2pm to 3:20pm. The first meeting was on Monday March 31, and the last meeting will be on Wednesday June 4, 2003.

Each meeting of 80 minutes will be divided into two parts. First, a student will give a talk lasting about 60 minutes presenting a recent technical paper in detail. In questions during the talk, and in the final 20 minutes, all seminar participants will discuss the paper and the issues raised by it.


date	presenter	paper	discussion board	slides
March 31	organizational meeting
April 2	no meeting
April 7	Coleman Mosley	Chapter 7 on the Luduan document retrieval system, in Finding Structure in Text, Genome, and Other Symbolic Sequences by Ted E. Dunning, 1998	here	here
April 9	Ari Frank	Cluster validation by prediction strength by Robert Tibshirani, Guenther Walther, David Botstein, Patrick Brown, 2001	here	here
April 14	Eric Wiewiora	PolicyBlocks: An Algorithm for Creating Useful Macro-Actions in Reinforcement Learning by M. Pickett and A. G. Barto	here	here
April 16	Degui Zhi	Rational Kernels by Corinna Cortes, Patrick Haffner, and Mehryar Mohri, 2002	here	here
April 21	Dustin Boswell	Learning to Trade via Direct Reinforcement by John Moody and Matthew Saffell, 2001	here	here
April 23	Anjum Gupta	Adaptive Probabilistic Networks with Hidden Variables by John Binder, Daphne Koller, Stuart Russell, Keiji Kanazawa, 1997	here	here
April 28	Neil Jones	Shrinking Trees by Trevor Hastie, Daryl Pregibon, 1990	here	here
April 30	Doug Turnbull	Cobot: A Social Reinforcement Learning Agent by Charles Lee Isbell, Christian Shelton, Michael Kearns, Satinder Singh, Peter Stone, 2001	here	here
May 5	Honghao Shan	Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection by Peter N. Belhumeur, João P. Hespanha, David J. Kriegman, 1997	here
May 7	Lingyun Zhang	Visual features of intermediate complexity and their use in classification by Shimon Ullman, Michel Vidal-Naquet, Erez Sali, 2002	here	here
May 12	Bryant Forsgren	A Comparison of Event Models for Naive Bayes Text Classification by Andrew Mccallum, Kamal Nigam, 1998	here	here
May 14	Andrew Smith	Text Classification from Labeled and Unlabeled Documents using EM by Kamal Nigam, Andrew McCallum, Sebastian Thrun, Tom Mitchell, 2000.	here	here
May 19	Alex Simma	Maximum Entropy Discrimination by Tommi Jaakkola, Marina Meila, Tony Jebara, 1999	here	here
May 21	Junwen Wu	Theoretical Views of Boosting and Applications by Rob Schapire, 1999	here	here
May 26	no meeting
May 28	Junan Zhang	An adaptive version of the boost by majority algorithm by Yoav Freund, 1999	here	here
June 2	David Kauchak	Bursty and Hierarchical Structure in Streams by Jon Kleinberg, 2002	here	here
June 4	project presentations

Each student will do one term project following specific guidelines. The project should be at the frontier of current research, and preferably closely inspired by at least one of the papers discussed in the class. Project reports will be evaluated using these grading criteria. There is a schedule for handing in a detailed project proposal, a draft project report, and then the final report.

The seminar will have no final exam. Letter grades will be based mostly on the final project report, but the presentations, participation in class and in the web-based discussions, and the intermediate project deliverables are all important also.

The instructor is Charles Elkan, Associate Professor, whose office is AP&M room 4856. Feel free to send email to arrange an appointment, or telephone (858) 534-8897.

REGISTRATION

Students may take the seminar for a letter grade for four units, or for one or two units S/U:

For one unit, a student will present one research paper and participate in all class meetings.
For two units, a student will make two presentations.
Four units will require a presentation, participation in all class activities, and a project.

For four units, a student should register for CSE 254, section id 465982, for a letter grade. For one or two units, a student should register for the instructor's CSE 293, section id 466011.

Students who took the Spring 2001 or Spring 2002 version of CSE 254 may take it again. All papers will be different this year.

PAPERS AND TOPICS

In the first week, we will make a schedule of papers and presentations for the whole quarter. Papers will be recent technical articles, often from NIPS and ICML. Each paper will be made available on the web as the quarter progresses. Students will choose papers in consultation with the instructor. Relevant topics may include:

supervised learning with many classes
regularization when observed counts are zero or small
discriminative versus generative modeling of data
semi-supervised learning from labeled and unlabeled data
transductive inference
new boosting algorithms
feature selection from very large feature spaces
modeling human heuristics for learning
reinforcement learning algorithms and applications
applications to text categorization
applications to image retrieval
applications in computational biology
financial applications

Some papers will be theoretical, and some will be applied. Each presentation will cover a single conference paper, to ensure that it is explained and discussed in sufficient depth.

Various textbooks are useful as background reading. including

Machine Learning by Tom Mitchell, McGraw Hill, 1997, ISBN 0070428077.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction by T. Hastie, R. Tibshirani, and J. H. Friedman.

Students are encouraged to use other books and papers also.

PRESENTATIONS

The procedure for each student presentation is as follows:

One week in advance: Finish a draft of about 30 slides that present clearly the work in the paper. Make an appointment with the instructor to discuss the draft slides. Email the slides to elkan@cs.ucsd.edu.
Several days in advance: Meet for about one hour to discuss improving the slides, and how to give a good presentation.
Day of presentation: Give a good presentation with confidence, enthusiasm, and clarity.
Less than three days afterwards: Make changes to the slides suggested by the class discussion, and email the slides in PDF, two slides per page, to the instructor for publishing. Try to make your PDF file less than one megabyte.

Please read, reflect upon, and follow these presentation guidelines. Presentations will be evaluated, in a friendly way but with high standards, using this feedback form.

Each presentation should be prepared using LaTeX or Powerpoint, and should consist of about 30 slides. You must copy all important equations, diagrams, charts, and tables from the paper into your slides.

For each paper, we will have a web-based discussion area. Each student is expected to contribute at least one message to the discussion, before the presentation. A message may ask an interesting question, point out a strength or weakness of the paper, or answer a question asked by someone else. Messages should be thoughtful!

The schedule of presentations will be determined as much as possible on Monday March 31. Students should choose a date first, and then agree with the instructor about a paper to present. To find ideas, students can look at this list of possible papers and contact the instructor.

If you want to change your presentation date, please arrange a swap with another student and notify the instructor at least two weeks in advance.

Most recently updated on June 7, 2003 by Charles Elkan, elkan@cs.ucsd.edu