Machine Learning (CSE 6363) Fall 1997

Section 501, TuTh 7-8:20pm, 315 Nedderman Hall
University of Texas at Arlington
Department of Computer Science and Engineering

Description: A detailed investigation of current machine learning theory and methodologies. Introduces the background and basics of machine learning, including representation, inductive bias and performance evaluation. Analyzes and compares different machine learning methodologies, including statistical, connectionist, symbolic and optimization. Implementations of several methods will be provided for experimentation. Current issues in machine learning research and alternative learning methods will also be examined as they relate to course topics.

Prerequisites: Artificial Intelligence I (CSE 5360) and Artificial Intelligence II (CSE 5361).

Textbook: Tom M. Mitchell, Machine Learning, McGraw-Hill, 1997.

Grading: Five Homeworks (50%), Project (25%), Presentation (15%), Critiques and Class Participation (10/%).

Instructor: Larry Holder , 330 Nedderman Hall, 272-2596, holder@cse.uta.edu. Office hours: 5-6:30pm TuTh.

Course Details

Handouts

Machine Learning Resources

ML2.0 System is a framework for comparing several different learning algorithms. Written in C.


Lawrence B. Holder
Department of Computer Science and Engineering
University of Texas at Arlington
Box 19015, Arlington, TX 76019-0015
phone: (817) 272-2596, fax: (817) 272-3784
email: holder@cse.uta.edu