CSI 5v93: Machine Learning, Spring 2007


Wed Jan 3 12:23:41 CST 2007
Welcome to the course!


This is a course in machine learning, a subfield of artificial intelligence. Machine learning 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, or make inferences, or improve, from data. Further, we would like to use the learned models to make predictions about unknowns.

This course covers:

This list of topics is optimistic. Be prepared to invest the time necessary to understand the concepts, and to do the programming projects. My best advice is to attend the lectures, read the book, ask questions, and start projects early.

Practical information

Officially, lectures are from 11:00 AM to 11:50 AM in Rogers 210 on Mondays, Wednesdays, and Fridays. Unofficially, we will meet Mondays and Fridays for 75 minutes.

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 an aggressive schedule of the material we will cover:

Week Dates New topics Reading Monday Friday
1 Jan 8-12 Introduction, math & stats background, linear models 1, 2, Lin. Alg., Bishop 1 Homework 1
2 Jan 15-19 MLK holiday
3 Jan 22-26
4 Jan 29-Feb 2 Linear regression methods 3 Homework 2(LaTeX template)
5 Feb 5-9 Homework 3(LaTeX template)
6 Feb 12-16 Linear classification methods 4
7 Feb 19-23 Support Vector Machines 4.5, 12.1-12.3
8 Feb 26-Mar 2
9 Mar 5-9 Bayesian learning Mitchell 6 Homework 4
Mar 12-16 Spring break
10 Mar 19-23 Midterm
11 Mar 26-30 Unsupervised learning 14
12 Apr 2-6 Easter holiday
13 Apr 9-13 Easter holiday
14 Apr 16-20 Student presentations Homework 5
15 Apr 23-27 Student presentations
16 Apr 30-May 4 Student presentations Final exams

The final exam is on Monday, May 7th, between 9-11 AM. The latest university finals information is available here.

Textbooks & resources

Required text: we will be using The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani, and Jerome Friedman. You can purchase this book from the Baylor bookstore or amazon, among other places.

Optional texts:

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 © 2007 Greg Hamerly, with some content taken from a syllabus by Jeff Donahoo.
Computer Science Department
Baylor University

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