Statistics & Applied Probability - PSTAT

PSTAT 231 - Introduction to Statistical Machine Learning

Statistical Machine Learning is used to discover patterns and relationships in large data sets. Topics will include: data exploration, classification and regression trees, random forests, clustering and association rules. Building predictive models focusing on model selection, model comparison and performance evaluation. Emphasis will be on concepts, methods and data analysis; and students are expected to complete a significant class project, individual or team based, using real world data.

Prerequisites: PSTAT 120A-B; and PSTAT 126 with a minimum grade of C or better.
PSTAT 231
10 / 50 Enrolled
Introduction to Statistical Machine Learning
Katie Coburn 3.1
M W
17:00 PM - 18:15 PM
78.2% A
PSTAT 231
15 / 50 Enrolled
Introduction to Statistical Machine Learning
Coburn T
M W
17:00 PM - 18:15 PM
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