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Course Evaluation Results

Course Details

Spring 2018-2019
* ELE 486 / APC 486   Graded A-F, P/D/F, Audit

Transmission and Compression of Information

Christopher G. Brinton

Our digital world relies heavily on the ability to extract, store, and transfer information. Over the years much effort has been devoted to the development of methodologies that perform these tasks efficiently. This course covers the fundamental algorithms and limits of data compression and transmission, detailing key components of information theory and coding theory such as entropy, source/channel codes, and information measures. We also draw connections between these theories and several techniques in supervised and unsupervised machine learning, including data clustering, principal component analysis and graphical models.

Sample reading list:
T. Cover, J. Thomas, Elements of Information Theory
R. Gallager, Principles of Digital Communication
C. Bishop, Pattern Recognition and Machine Learning

Reading/Writing assignments:
9-10 problem sets during the semester. Student is expected to read 20 pages per week.

Mid Term Exam - 30%
Final Exam - 35%
Problem set(s) - 35%

Prerequisites and Restrictions:
Linear algebra and basic notions of probability..

Other information:
No textbook is required for this course, however, the textbooks can be used for additional reading.

Schedule/Classroom assignment:

Class numberSectionTimeDaysRoomEnrollmentStatus
41383 L01 11:00:00 am - 12:20:00 pm M W   Friend Center   112   Enrolled:9 Limit:20