CS 497 DNR
Computational Theories of Learning and
Meeting Times and Locations:
206 TRANS BLD
Professor: Dan Roth
Office: 2101 DCL
Office Hours: Thursday 1:30-2:30pm
Phone: (217) 244-7068
The purpose of the course is to acquaint students with the theoretical
foundations of machine learning and intelligent inference.
The focus this year will be on Inference. We will read
classical and recent papers that introduce and address
important issues in intelligent reasoning. Some of the papers will
address issues in knowledge representation and learning, that are
important for intelligent inference.
The two main foci will be:
In order to stay with our feet on the ground we will keep in the back of our
mind a concrete problem in natural language comprehension, and will try
to study how different approaches can be applicable to it.
- The study of knowledge representation and inference formalisms
that are amenable to mathematical analysis. In particular, we will
look at probabilistic approaches, propositional and relational (FOL)
representations, and on different inference formalisms within
those. We will also discuss whether, and in what way, different
formalisms make sense empirically.
- Ways to integrate theories of
learning with those of reasoning.
A more detailed description of topics and a list of papers is given in
Prerequisites The course is targeted at graduates and advanced
undergraduates. Ideally, students should have background in basic
theory of computation and algorithms, probability, and introductory
The course will not have any exam. Instead there will be several other
- Presenting a paper. Every student will have to present at least
one paper. This will require a deep understanding of the paper,
relating it to other approaches we discuss and addressing the
background problem within the approach presented in the paper.
- Writing a short (<1 page) critical survey for each of the papers
presented in class.
- A term paper. An experimental or theoretical paper in which an
inference approach is studied thoroughly with respect to the class'
background problem in language comprehension.
Articles will be distributed in class and, if possible, will be
available from the course home page http://L2R.cs.uiuc.edu/~danr/Teaching/CS497-01/
Some background material can be found in several books.
S. Russell and P. Norvig, Artificial Intelligence, A Modern Approach
Prentice Hall/Allyn\&Bacon, 1995, ISBN: 0-13-103805-2,
Call number: Q335.R86 1995
- D. Poole, A. Mackworth and R. Goebel, Computational Intelligence,
A Logical Approach. Oxford University Press, 1998, ISBN:
0-19-51-270-3, Call number: Q335.P657 1997
N. Nilsson, Artificial Intelligence: A New Synthesis
Morgan Kaufmann, 1998, ISBN: 1-55860-467-7,
Call number: Q335.N495 1998
E. Rich and K. Knight, Artificial Intelligence, 2nd ed.
McGraw Hill Book Company, 1991, ISBN: 0-07-052263-4,
Call number: Q335.R53 1991
M.R. Genesereth and N. Nilsson, Logical Foundations of Artificial
Intelligence Morgan Kaufmann, 1987, ISBN: 0-934613-31-1,
Call number: Q335.G37 1988 and Q335.G37 1987