The primary objective of this course is to present fundamental concepts and basic techniques of optimization with possible applications, which are essential for researches in circuit design, communications, signal processing, and control engineering. Topics include linear vector spaces and linear operators, linear estimation and filtering, functional analysis, optimal control, linear programming, nonlinear programming, dynamic programming, genetic programming (evolutionary computation), and neural networks.
This course is to provide EE students with understanding and ability for design and implementation of data structure for problems solving in the EE area using computer programming. It deals with information representation using data abstraction, object-oriented programming, Algorithm analysis. Basic data structures to be covered are Array and Linked list, Stack and Queue, Tree, Graph, Sorting, and Hashing. Applications of such basic structures in EE problems using C++ are also covered.
In this course, we discuss such various topics in probability theory and introductory random processes as probability, random variables, expectations, characteristic functions, random vectors, random processes, correlation functions, and power spectrum. From time to time, homework problems will be assigned, usually not for mandatory submission.