Curriculum

This goal of this course is to understand the basic principles of digital logic circuit, and the fundamental concepts, components and operations of digital system.

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

 In this course, students will learn to design/implement complicated system-level software in the C/C++ programming language and understand the internal architecture of unix/linux.

The objective of this course is to understand the basic principles and hardware structures of computer systems including personal computers and workstations, and to learn how to design computers. This course covers data representation, CPU organization, instruction classification, language processing of assemblers and compilers, pipelining for performance enhancement, memory hierarchy, cache memory, and IO peripheral devices. In addition, high-performance computer systems are to be introduced.

This course is a brief introduction to random processes. Topics include Basic operating principles and circuits of AM, FM, and SSB modulation/demodulation, PLLs, mixers, and ADCs; Noise performance of communication systems; Introduction to digital communication techniques such as BPSK, FSK and QAM keying/detections. Issues related to multiple access techniques are covered. (Prerequisite: EE202)

This course will help the students learn how to design and implement computer networks, and their protocols, services, and applications. This course will include both principles and practice, but more importantly, is designed to let the students have hands-on experience. Most of the topics will be connected to the Internet, i.e., how the Internet works.

This course deals with the foundational principles of distributed systems and covers modern distributed computing frameworks. Students learn abstractions and principles in distributed computing, how to design a distributed computing framework, and their application to cloud computing.

This lecture provides a short introduction to essential topics in information theory for communication engineers. The topics include 1) measures of information and source, 2) Data compression, 3) Channel Capacity and Error Control Codes, 4) a very short description of rate-distortion theory.

Introduces the principles, algorithms and application of machine learning from the point of modeling and prediction; learning problem representation.

This course will cover concepts such as representation, over-fitting, regularization, and generalization; topics such as clustering, classification, regression, recommendation problems, probabilistic modeling, reinforcement learning, and various on-line algorithms. It will also introduce a support vector machine and deep learning.

This course covers intermediate electromagnetic waves and antennas. Transmission line applications and waveguide fundamentals are introduced. Basic antenna theory is discussed. Antennas for wireless applications are further studied. (Prerequisite: EE204)