ANNOUNCEMENTS:

COURSE DESCRIPTION:

The introduction of Support Vector Machines (SVMs) in the
1990s led to an explosion of applications and deepening
theoretical analysis that have established SVMs as one of the
standard tools for machine learning and data mining. This course
provides a comprehensive introduction to SVMs and other kernel
methods, including theory, algorithms and applications. Topics
covered will be selected from the following: support vector
classification and regression; novelty detection and feature
extraction; non-linear dimensionality reduction; reproducing
kernel maps; regularization; statistical learning theory and
robust estimation; convex optimization and implementation;
kernel design and applications. Homework assignments will be a
mix of theory and programming.

BASIC INFORMATION:

INSTRUCTOR:

HANDOUTS:

ASSIGNMENTS:

TEXT:

ADDITIONAL REFERENCES:

BACKGROUND MATERIAL:

SOFTWARE:

PLAGIARISM AND CHEATING: