CmpE 621 Pattern Recognition
Fall Semester 2000-2001
Topics covered by lectures
- Week 1 Introduction
- Week 2 Statistics Review: Sampling, Parameter Estimation.
- Week 3 Statistics Review Cont'd: Interval Estimates,
Hypothesis Testing, Regression, Analysis of Variance. Bayesian
- Week 4 Multivariate Analysis Review. Parametric
Classification. 1st HW on Polynomial Regression, due Nov 2
- Week 5 Parametric Discrimination, Principal Components
Analysis, Logistic Discrimination. 2nd HW on Parametric
Discrimination and PCA.
- Week 6 Fisher's Linear Discriminant, Discrimination by
- Week 7 Nonparametric Methods for Probability Density
Estimation and Classification Kernel-based methods, k-nearest
3rd HW on Fisher's LD, Logistic Discrimination and Discrimination by
- Week 8 Nonparametric methods for Regression. Generalized
- Week 9 Unsupervised Learning. k-means. Gaussian mixtures and
Expectation-Maximization Algorithm.4th HW on Nonparametric
- Week 10 Hidden Markov Models. Baum-Welch algorithm.
- Week 11 Decision Trees. ID Trees, multivariate splits, error
correcting output codes.
- Week 12 Review of the course.
Bayes Decision Theory. Parametric and Nonparametric Methods. Linear Discriminant
Functions. Higher Order Discriminants with Emphasis on Artificial Neural
Network Based Learning Methods. Unsupervised Learning and Clustering. Case
Duda, R., Hart, P. (1973) Pattern Classification and Scene Analysis, Wiley.
Fukunaga, K. (1990) Introduction to Statistical Pattern Recognition, 2nd
Edition, Academic Press.
McLachlan, G. (1992) Discriminant Analysis and Statistical Pattern Recognition,
Schalkoff, R. (1992) Pattern Recognition: Statistical, Structural, and
Neural Approaches, Wiley.
Dr Ethem Alpaydin, Associate Professor. Department of Computer Engineering,
Bogazici University firstname.lastname@example.org
To introduce the student to the problems related to pattern recognition
and discuss how solutions may be attempted using statistical techniques.
This course is followed by and is a prerequisite for CmpE 545 Artificial Neural Networks.
Prerequisite by Topic
Undergraduate level calculus, probability theory. Prior experience in a
high-level programming language.
Introduction to Pattern Recognition
Bayes Decision Theory
Unsupervised Learning and Clustering
Almost all homeworks require computer simulations.
1 Project 0.30
1 Final 0.30