Machine Learning (COMP-652A)
Solution to homework 6 has been updated on Monday, December 2, 9pm, to fix
a problem in the solution to question 5a (a couple of trials were left out
in the counting). Thank you to all the people who pointed it out!
Lecture notes on K-means clustering and the exact references for
unsupervised learning (mentioned in class) were posted on the web page
on Monday, December 2, 2:45pm.
Solutions to homework 6 have been posted on Monday, December 2, 2pm.
The second examination will take place on Tuesday, December 3,
during class time, in McConnell room 103. Note the change in
A list of topics for the exam has been posted on
the exams web page, on Wednesday, November 27, 10:15pm.
The slides for the lecture on bagging have been posted on Wednesday,
November 27, 10pm, and the lecture notes on boosting have also been
slightly updated. Links to boosting/bagging applets are posted.
Homework 6 has been posted Friday, November 15, at 10:00pm. It is due
Friday, November 21, at 5:30pm.
Tutorial material on ensembles of classifiers, boosting and SVMs has
been posted on Wednesday, November 13, 3:00pm. It is linked next to
the corresponding lectures.
Homework 5 has been posted on Saturday, November 9, midnight. It is due
on Friday, November 15, by 5:30pm.
Reading 2 has been posted on Thursday, November 7. It is due on Tuesday,
Homework 4 has been updated to include Problem 4, on Friday, November
1, 1:45pm. Due to the delay, the homework has been extended until
Monday, November 4, 5:30pm.
Solutions to assignment 3 have been posted on Sunday, October 27, 10:00pm.
Solutions to assignment 2 have been posted on Sunday, October 27, 4:45pm.
Where: Peterson Hall, Room 306.
When: Tuesdays and Thursdays, 11:30-1:00.
What: The goal of this class is to provide an overview of
the state-of-art algorithms used in machine learning. The field of
machine learning is concerned with the question of how to construct
computer programs that automatically improve with experience. In
recent years, many successful applications of machine learning have
been developed, ranging from data-mining programs that learn to detect
fraudulent credit card transactions, to autonomous vehicles that learn
to drive on public highways. At the same time, there have been
important advances in the theory and algorithms that form the
foundation of this field. During this course, we will study both the
theoretical properties of machine learning algorithms and their
School of Computer Science
Office: McConnell 326
Tuesday and Thursday, 1:00-1:30pm.
Meetings at other times by appointment only
IMPORTANT: E-mail is the quickest way to reach me and get your questions answered.
- Textbook: Tom Mitchell, "Machine Learning", McGraw-Hill, 1997.
- Lecture notes and other relevant materials are available on this web page.
- Other reference materials will be distributed in class as needed.
Last modified: Mon Dec 2 14:52:02 EST 2002