ECE 566: COMPUTATIONAL INFERENCE AND LEARNING, FALL 2019
Computational inference and machine learning have seen a surge of interest in the last 15 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 (Detection and Estimation), 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.
Class time and place: 1400 - 1520 TR, 3081 Electrical Engineering Building.