Computational Theories of Learning and Reasoning

Fall 1999

Course Information

Meeting Times and Locations:

Lecture: 0.75 unit, Tue/Thu 9:30 - 10:45, 3262 DCL


Professor: Dan Roth
Office: 2101 DCL
Office Hours: Thursday 1:30-3pm
Phone: (217) 244-7068

Course Description

The purpose of the course is to acquaint students with the theoretical foundations of machine learning and intelligent inference. Along with providing an introduction to this field the emphasis will be on providing familiarity with some topics in current research. The focus, beyond introducing the main computational models, would be two fold:
  1. The study of learning algorithms that are both amenable to mathematical analysis and make sense empirically (in terms of performance and scalability) and
  2. Ways to integrate theories of learning with those of reasoning,
  3. Here is a tentative plan of the course.


The course is targeted at graduates and advanced undergraduates. Ideally, students should have background in basic computation theory and algorithms, basic combinatorics and probability, and introductory AI.


The course will not have any exam. Instead there will be several other requirements:
  1. Scribe notes: Each lecture one of the students will be assigned the job of "scribing" the lecture, for later distribution to the class. The notes should not be simple copy of the what is written on the board. It has to be written so that it reflects understanding of the material.
    A latex form as well as draft scribe from last year (for some of the lectures) will be available here.
  2. Presentation: All the students will present a lecture in class. The material for the lecture will be from a research paper. A preliminary list of papers is available.
  3. All students will write a short (half a page) review of the target paper.
  4. In addition, there may be 1-3 non-obligatory problem sets on the introductory material. The goal is to help you make sure you understand the material.

Course Materials

The articles will be distributed in class. Lecture notes and handouts will be available from the course home page

Dan Roth