COSC 6342: Machine
Learning, Fall 2007
|
General
Information |
Instructor:
Ricardo Vilalta (vilalta@cs.uh.edu)
Office:
PGH 573
Telephone:
(713) 743-3614
Textbook:
“Pattern Recognition and
Machine Learning” by Christopher M. Bishop; 1st Edition. Springer, 2006.
Additional
“Pattern
Classification” by Duda, Hart, and Stork; 2nd
Edition, Wiley-Interscience, 2000.
“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 TA (Teacher Assistant) |
Student:
Girish
Nandagudi (nitinsgirish@yahoo.co.uk)
Office:
PGH
374
Office Hours:
Tuesdays 11:00 AM – 1:30 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 |
60% |
|
Homework |
20% |
|
Project |
20% |
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 5 |
1st Midterm Exam |
|
November 16 |
2nd Midterm Exam |
|
November 21-24 |
No class (Thanksgiving
Holiday) |
|
November 30 |
Submit Project Report |
|
Note: There is no final exam
in this course. |
|
|
Schedule |
|
Dates |
Topic |
|
August 24 |
Introduction* |
|
August 31 |
Probability
and Information Theory |
|
September 7 |
Linear Models for
Classification |
|
September 14 |
Decision
Trees |
|
September 21 |
Neural
Networks |
|
September 28 |
Kernel Methods* |
|
October 5 |
Midterm Exam 1 |
|
October 12 |
Graphical Models |
|
October 19 |
Mixture Models and EM |
|
October 26 |
Sampling Methods |
|
November 2 |
Combining Models |
|
November 9 |
Reinforcement Learning* |
|
November 16 |
Midterm Exam 2 |
|
November 30 |
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