University of Wisconsin Madison
Theory and Applications of Pattern Recognition (M E 532) Syllabus
Course Learning Outcomes
    Course Learning Outcome
  • 1
    Understand the theory and methods for learning from data, with an emphasis on pattern classification.
Theory and Applications of Pattern Recognition
M E 532 ( 3 Credits )
Pattern recognition systems and components; decision theories and classification; discriminant functions; supervised and unsupervised training; clustering; feature extraction and dimensional reduction; sequential and hierarchical classification; applications of training, feature extraction, and decision rules to engineering problems.
ECE 331 or Math 431 or cons inst
College: College of Engineering
Instructor Name
Instructor Campus Address
Contact Hours
Course Coordinator
R. Nowak
Text book, title, author, and year
Pattern Classification and Scene Analysis, R. Duda, P. Hart and D. Stork, John Wiley, 2001.
Supplemental Materials
Required / Elective / Selected Elective
ABET Program Outcomes Associated with this Course
Program Specific Student Outcomes
Brief List of Topics to be Covered

Bayesian decision theory

Nonparametric methods

Linear discriminant functions

Decision Trees

Learning theory

Additional Information
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