Academics

Graduate Program

Statistical Learning Theory

Subject No.
Research
Credit
Classification
Prerequisite

Introduce students the fundamental concepts and intuition behind modern machine learning techniques and algorithms, beginning with topics such as perceptron to more recent topics such as boosting, support vector machines and Bayesian networks. Statistical inference will be the foundation for most of the algorithms covered in the course.

 

Recommend

Signal
EE.40074

This course introduces students to a variety of media elements including text, graphics, sound, video, hardware and software components and the necessity for interactivity in multimedia as well. By introducing associate fundamental technologies, the course aims to help and encourage students to develop their imaginative and creative skills using multimedia. (Prerequisite: EE202)

 

Recommend

Prerequisite

Communication, Signal
EE.20002

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.

Recommend

Prerequisite

Wave
EE.20004

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.

Recommend

Prerequisite

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

Recommend

Prerequisite

Computer, Circuit, Communication, Signal, Wave, Device
EE.30005

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

 

Recommend

Prerequisite