CSCI1950-F(Formerly CS195-5 )
Intro. to Machine Learning
|Meeting Time:||K: TTh 2:30-3:50|
|Exam Group:||11; 05/10/2013 Exam Time: 09:00 AM|
|Offered This Year?||Yes|
|When Offered?||Most Years|
How can artificial systems learn from examples, and discover information buried in massive datasets? This course explores the theory and practice of statistical machine learning. Topics include parameter estimation, probabilistic graphical models, approximate inference, and kernel and nonparametric methods. Applications to regression, categorization, and clustering problems are illustrated by examples from vision, language, communications, and bioinformatics. Prerequisites: CSCI0160, CSCI0180 or CSCI0190, and comfort with basic probability, linear algebra, and calculus.