CSI 5325: Introduction to Machine Learning, Spring 2012


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 9:05 to 9:55 AM in Rogers 210 on Mondays, Wednesdays, and Fridays.

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.


Semester project

Here are guidelines on the semester project.


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

The midterm exam will be in class on Friday, February 24.

The final exam will be on Friday, May 4 at 4:30 PM. For the latest university finals information, check here.

Textbooks & resources

Some of the primary material will be based on the topics given at Andrew Ng's CS 229 course at Stanford. In particular, you should refer to the handouts (lecture notes, review notes, Matlab tutorials).

We will also be using Machine Learning by Tom Mitchell for some of the material.

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

Other 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 © 2012 Greg Hamerly, with some content taken from a syllabus by Jeff Donahoo.
Computer Science Department
Baylor University

Valid html and css.