Academics

Graduate Program

Academics

Graduate Program

Graduate Program

Neural Networks

Subject No.
Research
Credit
Classification
Prerequisite
EE538
Signal
3
72

This course covers the theory and application of neural networks. In particular, lectures explore the structure and function of neural networks and their learning and generalization. Also, various models of neural networks and their applications are illustrated.

 

Recommend

Computer, Communication, Signal
EE528

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})

This course deals with various matrix computation algorithms for signal processing such as linear system solving, matrix norm, positive-definite matrix. Toeplitz matrix, orthogonalization/diagonalization, eigenvalue problems, SVD (singular value decomposition), iterative methods for linear systems, and so on.