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ECE 598 YW - Information-theoretic methods in high-dimensional statistics

Spring 2021

Official Description

Subject offerings of new and developing areas of knowledge in electrical and computer engineering intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: May be repeated in the same or separate terms if topics vary.

Course Director


The goal of this course is to understand the fundamental limits of high-dimensional statistical problems via information-theoretic methods. We will discuss foundational topics on information-theoretic methods, such as information measures, Fano's inequality, Le Cam's method and generalizations, metric entropy and volumetric methods, aggregation, as well as their applications on specific problems, such as sparse linear regression, estimating high-dimensional matrices, principal component analysis, functional estimation, statistical estimation on large alphabets and large graphs, etc.

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