Course Specifications for
CS 391L: Machine Learning
When & Where: Fall 2007, TuTh 2:00-3:30PM, BUR 136
Unique Number: 56790
Professor: Ray Mooney, TAY
4.130B, 471-9558, firstname.lastname@example.org
Office Hours: Tu, Th 9-10 AM or by appointment
Teaching Assistant (TA):
TA Office Hours:
Wed 12:00-1:00PM, Fri 1:30-2:30PM (Location: ENS 31NQ Desk#3)
- Class Email Alias: cs391L@cs.utexas.edu
Prerequisites: Basic knowledge of artificial intelligence
topics in search, logic, and knowledge representation (such as CS 381K) and Java Programming.
Textbook: Tom Mitchell,
Machine Learning, McGraw Hill, 1997.
Recommended Supplementary Texts:
Ian H. Witten & Eibe Frank,
Data Mining: Practical Machine Learning Tools and Techniques with Java
Implementations, Morgan Kaufmann, 1999.
T. Hastie, R. Tibshirani, & J. H. Friedman,
The Elements of Statistical Learning : Data Mining, Inference, and Prediction
Springer Verlag, 2001.
Richard O. Duda, Peter E. Hart, & David G. Stork,
Elements of Machine Learning,
Morgan Kaufman Publishers, San Fancisco, CA, 1995.
S. M. Weiss & C. A. Kulikowski,
Computer Systems that Learn,
Morgan Kaufman Publishers, San Fancisco, CA, 1991.
J. W. Shavlik &
T. G. Dietterich (Eds.),
Readings in Machine Learning,
Morgan Kaufman Publishers, San Fancisco, CA, 1990.
The intent of this course is to present a broad introduction to Machine
Learning, the study of computing systems that improve their performance with
experience, including discussions of each of the major approaches (see the course syllabus). The primary focus of the course
will be on understanding the underlying algorithms used in various learning
systems. Class lectures will discuss general issues as well as present
abstract algorithms. Implemented versions of many of the algorithms will be
provided in order to give a feel for how the systems discussed in class "really
work" and allow for experimentation.
Course Requirements and Grading
Chapters from the text and possibly other readings will be assigned throughout
the semester, and the reading should be done before the corresponding class.
Copies of the class lecture slides are available from the course home page. There will be about four or five
homework assignments (about one every two to three weeks early on) as well as a
The homework assignments will generally be programming assignments that involve
experimenting with or extending an existing machine learning system. The class
will use the Weka package
of machine learning software in Java. The code for the local version of Weka
used in class is in in
/u/mooney/cs391L-code/weka/ . See the
Tutorial.pdf file in this directory. If you do not know Java, you will need to
learn it on your own. You can use your student account on the department
workstations or any other Java platform available to you (however, we will only
provide support for running on departmental Unix machines). If you are not a
CS student and need a temporary department account, apply on the web at
The final project can be a more ambitious experiment or enhancement involving
an existing system or a new system implementation. In either case, the
implementation and/or experiments should be accompanied by a short paper (about
6 to 7 single-spaced pages) describing the project. A list of suggested
projects as well as an outline for the project report are available from the course home page. About half-way through the semester
you will be asked to submit a one-page project proposal.
Late Submission and Cheating Policies
Homework assignments should be completed independently by each student
and any program code should always be appropriately commented. Assignments are
due at the beginning of class on the due date. Be sure to hand in assignments
on time, late penalties are a loss of a percentage of the original overall
points for the assignment: 1 Day: 15%, 2 Days: 40%; 3 Days: 75%; past 3 days:
100%. A day is a 24 hour period starting at the beginning of class and
includes all weekend days and holidays.
Final projects are ideally done by a team of 2 students; however, group sizes
of 1 or 3 are possible with approval. Team members should strive to contribute
equally to the project and each member should submit a specific individual
statement declaring what part of the overall project work they performed.
Read the department's academic policy page at
http://www.cs.utexas.edu/users/ear/CodeOfConduct.html. Students who
demonstrably violate the Academic Honesty policy will receive a failing grade
in the class.
The final grade will be computed as follows:
33% Final Project
7% Class Participation