COSC 6342: Machine Learning, Spring 2012
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General
Information |
Instructor:
Ricardo Vilalta (vilalta@cs.uh.edu)
Office:
PGH 573
Office Hours:
Tuesdays and Thursdays 3:00 -
4:00 PM
Class time and room
location:
Tuesdays and Thursdays 4:00 -
5:30 PM; AH 106 Building
Telephone:
(713) 743-3614
Textbook:
Pattern Classification by R. Duda, P. Hart, and D. Stork; 2nd
Edition, Wiley-Interscience, 2001.
Additional Readings:
Pattern Recognition and Machine Learning by Christopher Bishop; 1st Edition. Springer,
2007.
The Elements of Statistical Learning by T. Hastie, R. Tibshirani, and J. Friedman; 2nd
Edition. Springer, 2009.
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Information TAs (Teacher Assistants) |
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Student: Kinjal Dhar Gupta |
Student: Bangsheng Sui |
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Office: PGH 313 Office Hours: Wednesdays 11:00 AM - 1:00
PM Email: kinjaldhargupta@gmail.com |
Office: PGH 313 Office Hours: Tuesdays 2:00 - 4:00 PM Email: suibangsheng@gmail.com |
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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 discriminants, neural
networks, decision trees, support vector machines, unsupervised learning,
reinforcement learning, etc.
For more information visit
the course on WebCT.
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Grading |
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Graded Work |
Weight |
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2 Midterm Exams |
70% |
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Home works |
30% |
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Calendar |
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Dates to remember |
Events |
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March 1 |
1st Midterm Exam |
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March 13, 15 April 17, 19 |
No class (Spring Holiday) No class |
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April 26 |
2nd Midterm Exam |
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Note: There is no final
exam in this course. |
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Schedule |
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Dates |
Topic |
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January 17,19 |
Introduction to Machine
Learning |
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January 24,26 |
Probabilistic
Learning |
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January 31, February 2 |
Linear Discriminants |
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February 7, 9 |
Neural
Networks |
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February 14, 16 |
Decision
Trees |
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February 21, 23 |
Support Vector Machines |
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February 28, March 1 |
Exam Preparation and
Midterm Exam 1 |
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March 6, 8 |
Ensemble Learning |
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March 13, 15 |
Spring Holiday |
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March 20, 22 |
Unsupervised Learning |
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March 27, 29 |
Evolutionary Learning |
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April 3, 5 |
Reinforcement Learning |
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April 10, 12 |
Graphical Models and
Bayesian Networks |
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April 24, 26 |
Exam Preparation and
Midterm Exam 2 |
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Files for
Downloading and Additional Class Information |
Connect to the class through WebCT