Curriculum
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Descriptions
COMP_SCI 396, 496: Statistical Machine Learning

This course is not currently offered.

Prerequisites

COMP_SCI 214

Description

This course introduces statical machine learning methods from a theoretical perspective. Topics include the maximum likelihood inference, regularization principle, risk minimization framework, cross-validation, high dimensional inference and nonparametric methods. 

COURSE INSTRUCTOR: Prof. David Demeter

REQUIRED TEXTS: None;

COMPUTER USAGE: The python and R programming language

GRADING: TBD

COURSE OUTCOMES: When a student completes this course, s/he should be able to:

  • Understand the state-of-the-art of statistical machine learning Become familiar with some fundamental principles of machine learning methods