COSC 6342: Machine
Learning, Fall 2008
|
General
Information |
Instructor:
Ricardo Vilalta (vilalta@cs.uh.edu)
Office:
PGH 573
Telephone:
(713) 743-3614
Textbooks:
“Pattern Recognition and
Machine Learning” by Christopher M. Bishop; 1st Edition. Springer,
2006.
“Pattern Classification” by
Duda, Hart, and Stork; 2nd Edition, Wiley-Interscience, 2000.
Additional
“Introduction to Machine
Learning” by Ethem Alpaydin; 1st Edition. MIT Press, 2004.
“Machine Learning” by Tom
Mitchell; 1st Edition., McGraw-Hill, 1997.
“Computer Systems that Learn”
by Kulikowski and Weiss; 1st. Edition, Morgan Kaufmann,1991.
|
Information TAs (Teacher Assistants) |
|
Student: Girish Nandagudi
(nitinsgirish@yahoo.co.uk) |
Student: Anu Goyal
(anu.goyal@hotmail.com) |
|
Office: PGH
201 Telephone: 832-964-8082 Office Hours: Mondays
and Fridays 1 PM to 5PM. |
Office: PGH
567 Telephone: 281-935-5778 Office Hours: Tuesdays
and Thursdays 9 AM to 11 AM and 2
PM to 4 PM. |
|
Course
Description |
Machine Learning is the study
of how to build computer systems that learn from experience. It is a subfield
of Artificial Intelligence and intersects with statistics, cognitive science, information
theory, and probability theory, among others. The course will explain how to
build systems that learn and adapt using real-world applications from industry
and science (e.g., learning to classify astronomical objects, to predict
medical diagnoses, to play chess, etc.).
The class will be
self-contained (i.e., I will not assume any previous knowledge); a review
session on probability and information theory will precede those chapters in
need of background knowledge. Main topics include Linear Models for
Classification, Decision Trees, Neural Networks, Kernel Methods (Support Vector
Machines), Graphical Models, Mixture Models and EM, Sampling Methods, Combining
Models, and Reinforcement Learning.
Important Notice: This is a
hybrid course. Lectures will only be provided on the dates marked below.
Students will have access to a video recording on each class that can be
downloaded through the web.
For more information visit the course on VNET.
|
Grading |
|
Graded Work |
Weight |
|
2 Midterm Exams |
70% |
|
Project |
30% |
The project will be a report
on some area in machine learning you find most interesting.
You can either report on some novel
experiments after applying an algorithm on a database or attempt a theoretical
analysis.
The report must include a
short survey of related work with the corresponding list of references.
|
Calendar |
|
Dates to remember |
Events |
|
October 3 |
1st Midterm Exam |
|
November 21 |
2nd Midterm Exam |
|
November 28 |
No class (Thanksgiving
Holiday) |
|
December 3 |
Submit Project Report |
|
Note: There is no final exam
in this course. |
|
|
Schedule |
|
Dates |
Topic |
|
August 29 |
Introduction |
|
September 5 |
Probability
and Information Theory |
|
September 12 |
Linear Models for
Classification |
|
September 19 |
Decision
Trees |
|
September 26 |
Neural
Networks |
|
October 3 |
Midterm Exam 1 |
|
October 10 |
Kernel Methods* |
|
October 17 |
Graphical Models |
|
October 24 |
Mixture Models and EM |
|
October 31 |
Sampling Methods |
|
November 7 |
Combining Models |
|
November 14 |
Clustering |
|
November 21 |
Midterm Exam 2 |
|
December 3 |
Final Report |
*This is a hybrid
course. Lectures will only be provided on the dates marked in bold.
|
Files for
Downloading and Additional Class Information |
Connect to the class through VNET
You will need the following:
Software (free):
3IVX (3IVX.COM)
Quicktime (Quicktime.com)
Availability:
New lecture(s) every Friday
afternoon
(corresponding to that week’s
lecture)