ECE 566: COMPUTATIONAL INFERENCE AND LEARNING, FALL 2021
Computational inference and machine learning have seen a surge of interest in the last 20 years, motivated by applications as diverse as computer vision, speech recognition, analysis of networks and distributed systems, big-data analytics, large-scale computer simulations, and indexing and searching of very large databases. This new course will introduce the mathematical and computational methods that enable such applications. Topics include computational methods for statistical inference, information theory, sparsity analysis, approximate inference and search, and fast optimization.
The course will complement ECE561 (Statistical Inference for Engineers and Data Scientists), ECE544NA (Pattern Recognition and Machine Learning), and ECE543 (Statistical Learning Theory) which introduce core theory for statistical inference and machine learning respectively, but do not focus on computational methods. Teaching materials include notes from the instructor and articles from scientific journals.
Prerequisites: ECE490 and ECE534.
The course will be fully virtual this semester. Please find the Zoom meeting links for the Class meetings and office hour below. The meeting passwords can be found in the document: Meeting Passwords
Class time and place: 2:00-3:30 PM TR, Class meeting link