Graduate (List)

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 explains how digital signal processing techniques can be applied in the field of speech communication. The initial part of the course covers some background material in signal processing and the acoustic theory of speech production. Later lectures cover coding, recognition, and synthesis of speech. (Prerequisite: EE202)

This course handles underlying background theories for pattern recognition (PR) which is the start point for AI. It covers PR systems, Bayesian Classifier, likelihood-based PR, Discriminant Function-based PR, Support Vector Machine, NN-based PR, and other PR theories such as fuzzy theory, and so on.

This course deals with the fundamental concept of digital image processing, analysis, and understanding. Topics include sampling, linear and nonlinear operations of images, image compression, enhancement and restoration, reconstruction from projections, feature extraction, and image understanding.

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.

This course is designed to treat electromagnetic theory with applications in waveguides and antennas. The course will start with Maxwell’s equations and show how to apply Maxwell’s equations to the basic electromagnetic wave phenomena.

This course is designed to provide in-depth understanding and knowledge on the theory and applications of microwave circuits, components, and systems used in Microwave and RF wireless communication systems.
(Prerequisite: EE204)

This course mainly deals with general theories and applications for antenna and antenna system. The main topics are including an introduction to antennas, analysis, and synthesis of antenna elements and arrays, microstrip antennas, active phased array antenna, and smart antenna techniques.

This course provides an introduction to the fundamentals of quantum mechanics, tailored for undergraduate and graduate students who are new to the subject. It is particularly suitable for nonphysics majors seeking to gain a solid foundation in quantum mechanics.

This course covers fields and sources in waveguides, coupled mode theory, and wave propagation in periodic structures and anisotropic media. Green’s functions and their applications to radiation and scattering of waves are extensively considered.