Academics

Graduate Program

Academics

Graduate Program

Graduate Program

Linear Systems

Subject No.
Research
Credit
Classification
Prerequisite
EE581
Communication, Signal
3
72

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.

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})

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.

 

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.