Statistical Learning Theory

Two major themes of this course are ‘Modern Control System’ and ‘Computational Intelligence’. Each lecture will address a balanced emphasis on the theory about the control system and its applications in practice. The first part of this course includes digital control system design and state-space methods for control system design. The basic system identification scheme will also be included, considering the control of unknown systems. Once background knowledge of the modern control system is established, this course will then focus on the second part composed of computational intelligence using fuzzy logic, artificial neural network, and evolutionary computation as main topics to introduce recent trend in intelligent control. Term projects will be assigned to test the algorithms to the given problems. (Prerequisites: EE381)

This course is an introduction to continuous-time and discrete-time signals and systems. The course covers Fourier series, Fourier transform, Laplace transform, and z-transform. Various types of systems with emphasis on linear time invariant system is studied.

This course covers introductory electromagnetic fields and waves. Static electric fields and static magnetic fields are discussed. Time-varying fields and Maxwell’s equations are introduced. Waves and transmission lines are studied.

This course covers data structures, algorithms, JAVA for electron electronics engineering. We study object-oriented programming techniques and use programming language C, JAVA.

Experiments related to electronics are performed. Focus is made for both hands-on experience and design practice. (Prerequisite: EE201, EE304)