Information Theory

The main goal of this course is to present advanced topics of convex optimization which are essential for researches in communications and networks, estimation and signal processing, data analysis and modeling, statistics and finance, electronic circuit design, automatic control, and industrial engineering and to deal with their application areas. We study the primal-dual interior point method, semi-definite programs, and second-order cone programs.

In this course, based on the fundamental concepts and knowledge addressed in EE210, we discuss advanced topics in probability and random processes for applications in engineering. Topics include algebra of sets, limit events, random vectors, convergence, correlation functions, independent increment processes, and compound processes. (Prerequisite: {EE210} or {Approval of the Instructor})

Topics include system representation (input-output description, state variable description), solutions of linear dynamical equations, controllability and observability, irreducible realization, stability (BIBO stability, Lyapunov stability) for a rigorous treatment of linear systems. In addition, feedback linearization is to be covered.