Lecture: 3 hours
Project: 1 hour
Prerequisite: Course ECS170
Grading: Letter; paper reviews (20%), project (50%), final (30%)
Catalog Description:
Artificial intelligence techniques for knowledge acquisition by computers.
Fundamental problems in machine learning and discovery. Systems that learn
from examples, analogies, and solved problems. Systems that discover numerical
laws and qualitative relationships. Projects centering on implementation
and evaluation.
Goals:
Machine learning is concerned with the question of how to construct computer
programs that automatically improve with experience. The goal of this course
is to present the key algorithms and theory that form the core of machine
learning. The course tries to strike a balance between theory and practice.
Expanded Course Description:
Textbook:
T.M. Mitchell, Machine Learning, McGrawHill, 1997
Project:
Each student is expected to do a project using one of the machine learning
algorithms described in the text book and applying the method to solve a
realistic problem. The student is strongly advised to pick a problem domain
that is very familiar to him/her and has knowledge about where to get the
data. A student's proposed thesis or dissertation topic is the best area
to choose such a subject area and ask the question, "what if I solve
this problem using machine learning?" As each student is free to choose
his/her own machine Learning method before the classes begin, it is important
that the student has some idea as to the method(s) under consideration.
The student is then expected to write a project report by following the same general guidelines one follows in writing a conference paper. Typically, this paper shall be between 6-10 pages long (not counting any program listings the student may wish to submit as a backup) and should contain all information necessary to understand the paper and replicate the results - if one so wishes.
Instructors: K. Levitt, R. Vemuri
Prepared by: R. Vemuri (September 2002)
Overlap Statement:
This course does not have a significant overlap with any other course. It
covers some of the topics as in EEC 207, but does so at a more software-related
level. Applications in computational science are emphasized throughout.
2/02