Course Title:
Applications of Information Theory to Computer Science
Course Description:
Introduces information theory and its applications to various computational disciplines. Covers the basic concepts of information theory, including entropy, relative entropy, mutual information, and the asymptotic equipartition property. Concentrates on applications of information theory to computer science and other computational disciplines, including compression, coding, Markov chains, machine learning, information retrieval, statistics, computational linguistics, computational biology, wired and wireless networks, and image and speech processing. The course is self-contained; no prior knowledge of information theory is required or assumed.
Fall Offering:
Lab/Coreq 1:
Spring Offering:
Lab/Coreq 2:
Summer Offering:
Lab/Coreq Remarks:
Summer 1 Offering:
Prerequisite 1:
Summer 2 Offering:
Prerequisite 2:
Cross-Listed Course 1:
Prerequisite 3:
Cross-Listed Course 2:
Prerequisite 4:
Cross-Listed Course 3:
Prerequisite 5:
Cross-Listed Course 4:
Prerequisite Remarks:
Undergraduate probability theory and/or permission of instructor.
Cross-Listed Course 5:
Repeatable:
N
