University of Wisconsin Madison
Theory and Applications of Pattern Recognition (E C E 532) Syllabus
Course Learning Outcomes
    Course Learning Outcome
  • 1
    Students will be able to derive features from a data set using either intuition or standard feature sets.
  • 2
    They will then be able to take a new sample and to classify the sample into one of a number of possible classes.
  • 3
    Students will appreciate the difference between supervised and unsupervised learning.
  • 4
    Students will be able to implement such classification systems in software and to asses the efficacy of their implementations.
Details
Theory and Applications of Pattern Recognition
E C E 532 ( 3 Credits )
Description
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.
Prerequisite(s)
ECE 331 or Math 431 or cons inst
Department: ELECTRICAL AND COMPUTER ENGR
College: College of Engineering
Instructor
Instructor Name
Instructor Campus Address
instructorEmail@emailaddress.edu
Contact Hours
2.5
Course Coordinator
WILLIAM SETHARES
Text book, title, author, and year

Pattern Classification, R. Duda, P. Hart and D. Stork, 2000

Supplemental Materials
None
Required / Elective / Selected Elective
Selected Elective
ABET Program Outcomes Associated with this Course
Program Specific Student Outcomes
 
Brief List of Topics to be Covered

1. Bayesian decision theory

2. Nonparametric methods

3. Linear discriminant functions

4. Learning theory

5. Basic Machine Learning

Additional Information
 
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