Prof. Mireille Boutin
 

ECE662/ CS662

Statistical Pattern Recognition and Decision Making Processes

Spring 2022

Tuesday, Thursday 3:00-4:15

EE224


Instructor: Prof Mireille Boutin


Course Description: This is a theory focused (not project-based) course on pattern recognition from a statistical standpoint. We view the problem of pattern recognition from the point of view of estimation theory, and present a unified framework for all classification methods. Grades are based on homework (60%) and midterm exam (40%) scores.


Prerequisites:

  1. •linear algebra

  2. •probability and statistics


Midterm Exam will be in class on Th. March 24, 2022.


References include:

  1. • Introduction to Statistical Pattern Recognition, by K. Fukunaga

  2. • The Elements of Statistical Learning, by Hastie, Tibshirani, and Friedman.

  3. • Stochastic Processes: Theory for application, by  R.G. Gallager

  4. • R.G. Gallager’s lecture notes Chapter 3

  5. • Support Vector Machines, by Steinwart and Christman.

  6. • Pattern Classification, by Duda, Hart and Stork.