CS 91.421/544 Machine Learning and Data Mining — Fall 2006

CS 91.421 & 91.544 Machine Learning and Data Mining, Fall 2007

Tuesdays, 5:30 p.m. – 8:00 p.m., 402 Olsen Hall

Prof. Gary Livingston
Computer Science Department
University of Massachusetts Lowell

Announcements

Hi. It sounds like not everybody received my email about test 3 being a
take-home test so that you can spend more time on the projects. Also, this
way we can talk more in class about solutions to the problems groups are
having.

Gary

Instructions for writing paper reviews

Regrade request instructions

Class Email discussion group: http://weblab.cs.uml.edu/mailman/listinfo/91_421_544

Books

Required: Machine Learning by Author: Tom Mitchell, ISBN: 0070428077, publisher: McGraw-Hill

Strongly recommended: Data Mining: Practical Machine Learning Tools and Techniques (Second Edition), by Ian Witten and Frank Eibe, ISBN: 0-12-088407-0, publisher: Morgan Kaufmann

Contact information:

	Office:	301B Olsen Hall
	Office hours:	Tuesdays, 2:00 – 4:30 p.m., and also by appointment
	Phone:	978-934-4694
	E-mail:	gary@cs.uml.edu

Practice tests:

Homework solutions: 1, 2

Schedule:

Class	Date	Topics	Required reading	Homework	Handouts and exercises
1	9/11	Introduction	Mitchell, Ch. 1, 2	HW1 (due 9/18)
2	9/18	Decision tree learning	Mitchell, Ch. 3	HW2 (due 9/25)	Decision tree learning exercise
3	9/25	Decision tree learning	Mitchell, Ch. 4	HW3 (due 10/2)
4	10/2	Neural network learning	Mitchell, Ch. 5	HW4 (due 10/9)	Neural network learning exercise 1
5	10/9	Neural network learning	Mitchell, Ch. 6		Neural network learning exercise 2
6	10/16	Test 1: classes 1 – 3 Evaluation
7	10/23	Evaluation of hypotheses and learning systems	Mitchell, Ch. 9	HW5 (due 10/30)	Evaluation exercise
8	10/30	Statistical hypothesis testing Naïve Bayes learning Overview of ensembles	Mitchell, Ch. 13	HW6 (due 11/6) Paper for HW6	Naïve Bayes exercise
9	11/6	Genetic algorithms Test 2	Mitchell, Ch. 9	HW7 (due 11/13) Paper for HW7
10	11/13	Support vector learning	SVM tutorial	HW8 (due 11/20)
11	11/20	Guest lecture and talk by Prof. Daniels: clustering and support vector clustering Applications	Clustering paper	Applications
12	11/27	Learning First-Order Rules Applications: First runs	Mitchell, Ch. 10	Applications
13	12/4	Applications: Feature construction	Paper	Applications
14	12/11	Reinforcement learning Applications: Your choice	Mitchell, Ch. 13	Applications
15	12/18	Applications: Final presentations