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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.

This course will expose students to a variety of theoretical tools that are useful in the context of high-dimensional probability and statistics. Students will learn to analyze the behavior of high-dimensional random vectors and matrices, and to utilize these insights to study statistical tasks motivated by large-scale datasets. We will explore how techniques from information theory can be used to derive converse results, and how techniques from random matrix theory and graph theory can be used to design estimators with theoretical guarantees. Topics will include information measures and inequalities, concentration inequalities, information-theoretic lower bounding techniques, sparse signal recovery, large-scale regression via leverage scores, inference tasks on large graphs, and dimensionality reduction. Prerequisite: Main prerequisite is ECE 534 (Random Processes) or equivalent course. ECE 563 (Information Theory) or some prior exposure to information theory thinking is recommended b