CSI 5325: Introduction to Machine Learning, Spring 2011


This is a course in machine learning, which is a broad, interesting, and fast-growing field. The central problem we address in this class is how to use the computer to make models which can learn, make inferences, or improve its behavior, based on observations about the world. Further, we would like to use the learned models to make predictions about unknowns.

Machine learning is related to artificial intelligence, but also uses a lot of computer science, statistics, logic, probability, information theory, geometry, linear algebra, calculus, optimization theory, etc. It would be good to brush up on these topics if they're rusty.

Practical information

Lectures are from 12:30 to 1:45 PM in Rogers 104 on Tuesdays and Thursdays.

My office hours are listed on my home page. I am glad to talk to students during and outside of office hours. If you can't come to my office hour, please email me to make an appointment at another time.


Here is a schedule of the material we will cover, which is subject to change:

Week Dates Topic Assignment Reading Tuesday Thursday
1 Jan 10-16 Introduction; concept learning Assignment 1 1, 2 Introduction Concept learning
2 Jan 17-23 Decision trees 2, 3 Concept learning Decision trees
3 Jan 24-30 Assignment 2 3 Decision trees Decision trees
4 Jan 31-Feb 6 Neural networks 4 Neural networks Neural networks
5 Feb 7-13 Evaluating hypotheses Assignment 3 4, 5 Neural networks Evaluating hypotheses
6 Feb 14-20 Bayesian learning 5, 6 Evaluating hypotheses Bayesian learning
7 Feb 21-27 6 Bayesian learning Bayesian learning
8 Feb 28-Mar 6 Learning theory 7 MIDTERM Learning theory
9 Mar 7-13 Spring break Spring break
10 Mar 14-20 Assignment 4 7 Learning theory Learning theory
11 Mar 21-27 Instance-based learning 8 Instance-based learning Instance-based learning
12 Mar 28-Apr 3 Support vector machines Burges paper; Alpaydin chapter; Schölkopf chapter Instance-based learning Support vector machines
13 Apr 4-10 Assignment 5 Burges paper; Alpaydin chapter; Schölkopf chapter Support vector machines Support vector machines
14 Apr 11-17 Unsupervised learning Duda et al. chapter 10 Unsupervised learning (pt. 2) Diadeloso
15 Apr 18-24 Boosting Hidden Markov models
16 Apr 25-May 1 Boosting Freund and Schapire Hidden Markov models Project presentation

The final exam will be on Wednesday, May 4 at 2:00 PM. For the latest university finals information, check here.

Paper reading/presentation component

As a part of the assignments, each student will present one paper on a topic which we are studying. Here is the list of topics, papers and people who are signed up.

Everyone in the class is expected to read the paper that is presented so we may have a fruitful discussion.

Textbooks & resources

Required text: we will be using Machine Learning by Tom Mitchell.

We will also be reading from some papers in the research literature.

Optional texts (I may draw some material from these):

Further online resources:


Grades will be assigned based on this breakdown:

Here is a tentative grading scale:
A: 90-100, B+: 88-89, B: 80-87, C+: 78-79, C: 70-77, D: 60-69, F: 0-59

Some projects may be worth more than others. Exams are closed-book. The final will be comprehensive.


Academic honesty

I take academic honesty very seriously.

Many studies, including one by Sheilah Maramark and Mindi Barth Maline have suggested that "some students cheat because of ignorance, uncertainty, or confusion regarding what behaviors constitute dishonesty" (Maramark and Maline, Issues in Education: Academic Dishonesty Among College Students, U.S. Department of Education, Office of Research, August 1993, page 5). In an effort to reduce misunderstandings in this course, a minimal list of activities that will be considered cheating have been listed below.

Copyright © 2011 Greg Hamerly, with some content taken from a syllabus by Jeff Donahoo.
Computer Science Department
Baylor University

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