Course Overview

Optimization techniques are used in all kinds of machine learning problems because in general we would like to minimize the testing error. This course will contain two parts. The first part focuses on convex optimization techniques. We discuss methods for least-squares, linear and quadratic programs, semidefinite programming, and others. We also touch theory behind these methods (e.g., optimality conditions and duality theory). In the second part of this course we will investigate how optimization techniques are applied to various machine learning problems (e.g., SVM, maximum entropy, conditional random fields, sparse reconstruction for signal processing applications). We further discuss that for different machine learning applications how to choose right optimization methods.

Learning Achievement

learn how to use optimization techniques for solving machine learning problems.

Competence

Course prerequisites

Grading Philosophy

Course schedule

Course type

Online Course Requirement

Instructor

Chih-Jen Lin

Other information

(College of Electrical Engineering and Computer Science) Graduate Institute of Networking and Multimedia,
(College of Electrical Engineering and Computer Science) Graduate Institute of Computer Science & Information Engineering

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