MATH574M
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MATH574M - Statistical Machine Learning
Course Description
Basic statistical principles and theory for modern machine learning, high dimensional data analysis, parametric and nonparametric methods, sparse analysis, shrinkage methods, variable selection, model assessment, model averaging, kernel methods, and unsupervised learning.
Min Units
3
Max Units
3
Repeatable for Credit
No
Grading Basis
GRD - Regular Grades A, B, C, D, E
Career
Graduate
Course Attributes
GIDP - STATD (Statistics and Data Science)
Course Requisites
Probability at the level of MATH 464, statistics at the level of MATH 363 or MATH 466, and linear algebra.
May be convened with
Component
Lecture
Optional Component
No
Typically Offered Main Campus
Spring