Courses
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Descriptions
COMP_SCI 396, 496: Statistical Machine Learning

Quarter Offered

Winter : 9-10:30 MW ; Liu

Prerequisites

This course requires a relative high mathematics level and the students are expected to have basic familiarity with maximum likelihood estimation, linear regression and logistic regression models.

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. Han Liu

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