INTRODUCTION TO MACHINE LEARNING AND STATISTICAL PATTERN CLASSIFICATION

STAT 451

Course Description

Pattern classification, regression analysis, clustering, and dimensionality reduction. For each category, covers fundamental algorithms and selections of contemporary, current state-of-the-art algorithms. Focus on evaluation of machine learning models using statistical methods. Statistical pattern classification approaches, including maximum likelihood estimation and Bayesian decision theory, algorithmic and nonparametric approaches. Practical use of machine learning algorithms using open source libraries from the Python programming ecosystem.

Prerequisties

MATH 320,321,340,341,375, graduate/professional standing, or declared in Statistics VISP

Credits

Offered

Not Applicable

Grade Point Average

3.63

1.90% from Historical

Completion Rate

99.26%

0.00% from Historical

A Rate

60.29%

5.77% from Historical

Class Size

136

32.52% from Historical

Instructors (2025 Fall)

Sorted by ratings from Rate My Professors

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