CS533/CmpE533
Pattern Recognition - Spring 2007-08
Class
Meeting Times |
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| Intructor:
Sohaib Ahmad Khan sohaib at lums dot edu dot pk http://web.lums.edu.pk/~sohaib TA: Syed Farooq Ali farooqali at lums dot edu dot pk |
Instructor
Office hrs: (Rm 407 Library Bldg) Mon 10:15 am - 11:45 am Wed 10:15 am - 11:45 am TA Office hrs: (Rm 437) TBA |
Course
Description: This course provides an introduction to the area of Statistical
Pattern Recognition. The course will be beneficial to graduate students intending
to pursue research in this area, as well as in applied fields which use pattern
recognition, such as speech recognition, computer vision, image processing,
signal classification, optical character recognition and data mining. Major
topics covered in the course include supervised and unsupervised learning, Bayesian
decision theory, parametric and non-parametric density estimation methods, linear
discriminant functions and clustering methods.
Text
Book
Pattern Classification (2nd Ed.), Richard O. Duda, Peter E. Hart, David
G. Stork, Wiley-Interscience 2001
Reference
Books
Statistical Pattern Recognition, 2nd Ed, Andrew Webb, Wiley 2002
Pattern Recognition and Machine Learning, Christopher Bishop, Springer 2006
Course Outline [PDF]
Useful
Links
Website of
Duda/Hart/Stork Textbook
Websites for reference texts: Webb,
Bishop
The Pattern Recognition
Files
A good set of lecture slides: PRISM
lectures
PR description and links on AAAI
website
A good
review paper of statistical pattern recognition by Jain et. al
published in 2000.
A good
glossary of statistical pattern recognition terms by Thomas Minka
| Date | Lecture Topics | Readings | Annoucements |
| Mar 17, 2008 | Lecture
1: Course Introduction and Policies. Introduction to Statistical Pattern
Recognition. Features and Classifiers. Supervised, Unsupervised and Reinforcement
Learning. Generative vs Discriminative Approaches. Estimation Error. Curse
of Dimensionality |
Ch 1 of DHS | Homework 0 assigned |
| Mar 19, 2008 | Lecture 2: Pattern Recognition design cycle, Minimum error rate classification for two category data, Bayes decision rule for two categories | Ch 1 and first four pages of Ch 2 of DHS |
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| Mar 24, 2008 | Lecture 3: Bayes Decision Theory for Continuous Features, Conditional Risk, Bayes Decision Rule for general case | Ch 2 of DHS | |
| Mar 26, 2008 | Lecture 4: Bayes Decision Rule (Two Category Case), Min Error Rate Classification, Minimax Criterion, Discriminant Functions | Sections 2.2.1, 2.3, 2.4 of DHS |
IMPORTANT:
Homework 1: DHS Ch2, Problems: 1, 2, 3, 4, 8, 9, 10, 12, 13, 14 Due Date: Thu Apr 3 at 5:00 pm |
| Mar 31, 2008 | Lecture 5: The Multivariate Normal Density, Eigen Vectors of the Covariance Matrix, Whitening Transformation | Section 2.5 of DHS | |
| Apr 2, 2008 | Lecture 6: Simultaneous Diagonalization for Two Normal Densities, Discriminant Functions of the Normal Density | Section 2.6 | |
| Apr 7, 2008 | Lecture 7: Discriminant Functions of the Normal Density (continued), Error Probabilities and Integrals, Chernoff Bound, Bhattacharyya Bound | Section 2.6, 2.7 | |
| Apr 9, 2008 | Lecture 8: Parametric Density Estimation, MLE, MLE of mean and variance of Gaussian Distribution | Sec 3.1, 3.2 | |
| Apr 14, 2008 | Lecture 9: Methods of Evaluating Estimators | Sec 3.2.4 and class notes | |
| Apr 16, 2008 | Lecture 10: Examples of computing Bias and MSE of estimators, Review for Midterm | Class notes |
IMPORTANT:
Homework 2: [pdf] Due Date: Mon Apr 28 at 5:00 pm |
| Apr 21, 2008 | Midterm
Exam 8:35 am in SC3 |
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| Apr 23, 2008 | Discussion on Midterm | ||
| Apr 28, 2008 | Bayesian Estimation | ||
| Apr 30, 2008 | Dimensionality Reduction: Principal Component Analysis | IMPORTANT: Homework 3 (Download handout and data) | |