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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.

Prerequisites

MATH 320 , MATH 321 , MATH 340 , MATH 341 , MATH 345 , MATH 375 , graduate/professional standing, or declared in Statistics VISP

Satisfies

This course does not satisfy any prerequisites.

Credits

Not Reported

Offered

Not Reported

Grade Point Average
3.4

-3.97% from Historical

Completion Rate
100%

0.67% from Historical

A Rate
42.68%

-23.37% from Historical

Class Size
82

-18.27% from Historical

Cumulative Grade Distribution

Instructors (2026 Summr)

Sorted by ratings from Rate My Professors

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