Fall 2018: Pattern Recognition [CS688]

General Description and Preliminary List of Topics:
Pattern recognition is concerned with the automatic finding of regularities in data and with the use of these regularities to take actions, such as classifying images or documents into different categories. The course covers key algorithms and theory at the core of pattern recognition. Particular emphasis will be given to the statistical learning aspects of the field. Topics include: decision theory, Bayesian theory, curse of dimensionality, linear and non-linear dimensionality reduction techniques, classification, clustering, kernel methods, mixture models and EM, deep learning.
Course Format:
Lectures by the instructor. Besides material from the textbook, topics not discussed in the book may also be covered. Research papers and handouts of material not covered in the book will be made available. Grading will be based on quizzes, an exam, and a project. Homework assignments will be given and require some programming. Exams and homework assignments must be done on an individual basis. Any deviation from this policy will be considered a violation of the GMU Honor Code.

Grading Policy:
Quizzes: 25%
Participation: 5%
Midterm: 35%
Project: 35% (Proposal 10%; Presentation 10%; Paper 15%)