**CS
407 Computational Learning Theory**
Course Syllabus -- Fall 1997
**Instructor: ** C. Tamon
Office: SC369
Phone: x6521
email: tino@sun.mcs
** Lecture Hours: ** M,W,F, 1:00p.m.
** Office Hours: ** M,W 2:00-4:00 p.m. and F 2:00-3:00 (tentative)
** Syllabus: ** This course is an introduction to computational learning
theory. Computational learning theory is an area that studies machine
learning problems from a mathematical perspective. Tentative topics that
will be covered include basic learning models (Valiant's PAC model, Littlestone's
online mistake-bound model, and Angluin's exact identification model),
simple learning algorithms (learning k-DNF, WINNOW and weighted majority,
closure algorithm, decision list), Occam's razor and the Vapnik-Chernovenkis
dimension, cryptographic and complexity hardness of learning, boosting
methods, and active learning of Finite Automata.
** Text: **
- Cormen, Leiserson, and Rivest.
* Introduction to Algorithms. *
MIT Press, 1990.
- Kearns, and Vazirani.
* An Introduction to Computational Learning
Theory.* MIT Press, 1994.
- Solow.
* How to Read and Do Proofs. * John Wiley and Sons, 1990.
** Grades: **
- Project/Presentation 30%
- Tests (2) 30%
- Assignments 40%
** Policies: ** Refer to ** Clarkson Regulations 1997 ** for policies
on Academic Integrity, Code of Ethics, Sanctions and other matters related
to plagiarism. |