University of Texas at Arlington
Computer Science Engineering
Fall 2000


CSE 6363 - Machine Learning
Section 501, TuTh 5:30-6:50pm, 315 Nedderman Hall
URL: http://www-cse.uta.edu/$^\sim$holder/courses/cse6363.html

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 II (CSE 5361).

Textbook:

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

Grading:

Six Homeworks (60%), Project (20%), Presentation (10%), Critiques and Class Participation (10%).

Instructor:

Larry Holder, 344 Nedderman Hall, 272-2596, holder@cse.uta.edu.
Office hours: TuTh 3:30-4:30pm or by appointment.

Course Details

The purpose of this course is to expose the student to several machine learning paradigms and provide in-depth understanding of selected methods. Homeworks will provide hands-on experience with selected learning methods and will test the student's understanding of topics discussed in class. All work in this course must be done individually.

The class project serves to further the student's understanding of one or more methods through an experimental or theoretical analysis of the learning methods. Students are required to obtain approval on a project proposal. Ideas for projects will be distributed in class.

Each student will make a class presentation of about 25 minutes on one or more technical papers in the area of machine learning to be approved by the instructor. The presentation must demonstrate an indepth understanding of the topic, including background material from the paper's references, and provide a critical review. Longer papers tend to be more self-contained; whereas, shorter papers will require more auxiliary material.

Lastly, the student is required to participate in class by submitting brief critiques of selected papers to be covered in class and participating in class discussions. The purpose of the critiques is to stimulate critical thinking and discussions on class topics. A critique is not merely a summary, but your own reactions to the content of the reading. Critiques should be approximately one page of single spaced type.

LATE POLICY: Homeworks may be turned in up to 48 hours beyond the due date for a 10% penalty. Only homeworks can be turned in late; all other work will receive no credit beyond the due date.

If you require an accommodation based on disability, please see me during the first week of the semester so we can be sure you are appropriately accommodated.

Schedule

Class	Date	Topics	Readings	Assignments Due
1	8/29	Introduction
2	8/31	Introduction	Ch1
3	9/5	Concept Learning	Ch2
4	9/7	Concept Learning
5	9/12	Decision Tree Learning	Ch3	HW1
6	9/14	Decision Tree Learning
7	9/19	Neural Networks	Ch4
8	9/21	Neural Networks
9	9/26	Evaluating Hypotheses	Ch5	HW2
10	9/28	Evaluating Hypotheses
11	10/3	Bayesian Learning	Ch6
12	10/5	Bayesian Learning
13	10/10	Bayesian Learning		HW3
14	10/12	Learning Theory	Ch7
15	10/17	Learning Theory
16	10/19	Instance-Based Learning	Ch8
17	10/24	Genetic Algorithms	Ch9	HW4
18	10/26	Learning Rule Sets	Ch10
19	10/31	Learning Rule Sets
20	11/2	Analytical Learning	Ch11
21	11/7	Combined Learning	Ch12	HW5
22	11/9	Combined Learning
23	11/14	Reinforcement Learning	Ch13
24	11/16	Student Presentations	papers*
25	11/21	Student Presentations	papers*	HW6
26	11/28	Student Presentations	papers*
27	11/30	Student Presentations	papers*
28	12/5	Current Issues	papers*
29	12/7	Conclusions
	12/12			Project

* Indicates that a written critique is due on that day covering papers to be distributed in class.