BLG 601E – Pattern Recognition, Spring 2010

Department of Computer Engineering , Istanbul Technical University

Instructor: Zehra Cataltepe Schedule: Wed. 9:00-12:00 (Room: ENGEL)

Please visit www.ninova.itu.edu.tr for slides, to submit your homeworks and other current information about the class.

Course Goals

Gain knowledge and practical experience in pattern recognition.

Gain knowledge about latest developments and applications in pattern recognition.

Learn practical skills and analytic background for building and enhancing pattern recognition applications.

Prerequisites

Probability, statistics, linear algebra.

Ability to use MATLAB to do the programming assignments, we will also use Weka.

Basic understanding of and familiarity with different Pattern Recognition concepts and algorithms.

If you have no familiarity, then you are advised to consider the MS level classes BBL514E or BLG527E.

Announcements:

[Feb 10] Welcome to the class! If you are not at ninova, please let me know your ITU email address so that I can add you.

Weekly Program (Please see the Ninova pages for slides)

Modified on March 8, 2010.

Week	Date	Content	Papers/slides (Please pick one)
1	Feb 10	Introduction, mathematical preliminaries Pattern Recognition basics [CB1]
2	Feb 17	Pattern Recognition basics [CB1]
3	Feb 24	Probability Distributions [CB2]
4	Mar 3	Linear Models for Regression [CB3]
5	Mar 10	Linear Models for Classification [CB4] HW1 Announced Papers announced
6	Mar 17	Neural Networks [CB5] HW1 Due, HW2 announced Papers determined
7	Mar 24	MIDTERM
8	Mar 31	Kernel Methods [CB6] Sparse Kernel Machines [CB7] HW1 due HW2 announced Projects determined
9	Apr7	Graphical Models [CB8]
10	Apr 14	Mixture Models and EM [CB9] Project plan due. Class 9-13
11	Apr21	Approximate Inference [CB10] Sampling Methods [CB11] HW2 due
12	Apr28	Continuous Latent Variables [CB12] Project discussion (preliminary report due) Class 9-13
13	May 5	Sequential Data [CB13]
14	May 12	Combining Models [CB14]
15	June 2 Wednesday (tentative, May change based on final exams)	Project Presentations (report and presentation due)

Grading (tentative):

Mıdterm	1	20%
Final	1	20%
Project	1	20%
Homeworks	3 (1 HW will be a paper presentation)	40% (15:hw1, 15:hw2, 10:paper)

 
References: 

• [CB] (Textbook) Chris Bishop, Pattern Recognition and Machine Learning, Springer 2006.
• [EA] Ethem Alpaydın, “Introduction to Machine Learning (Adaptive Computation and Machine Learning)”, The MIT Press, 2004.
• [DHS] Richard O. Duda, Peter E. Hart, and David G. Stork, Pattern Classification, 2nd Edition, 2000, Wiley.
• Recent papers on different pattern recognition topics.
• Matlab Introductory Material

• Matlab primer
• Getting started for Matlab
• Matlab tutorial
• A practical introduction to Matlab

• PRtools
• WEKA
• Introduction to Probability by -- Charles Grinstead and Laurie Snell.

Prepared by Zehra Cataltepe, February 2010.