Statistical Learning Theory

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

  • The aims of this course are to make the student understand the principles and fundamental concepts of circuit analysis; to develop the student's familiarity and understanding in modeling and analyzing circuits through a variety of real-world examples. Another important aim is to extend the student's ability to apply system analysis to other branches of engineering.

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

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

     

  • In this design experiment laboratory, knowledge learned in many other courses in this division are brought to bear on performing a project combining analog/digital and hardware/software. Hence, a chip stone design experiment will be performed, which establishes a synthesized application of undergraduate theory courses. For example, analog AM radio will be designed using various analog circuits, and voice recorder will be designed using Linux based embedded system.

  • In this course, we discuss such various topics in probability theory and introductory random processes as probability, random variables, expectations, characteristic functions, random vectors, random processes, correlation functions, and power spectrum. From time to time, homework problems will be assigned, usually not for mandatory submission.

    Recommend
  • Much of the basic discrete mathematical tools useful in electrical and computer engineering will be presented, with applications. Students will learn actively the art of creating real-world proofs in these areas, preparing them for diverse regions of electrical and computer engineering such as communication, architecture, networking, algorithms, cryptography, etc.

    Recommend
  • This course will cover general methods for analysis and design of the dynamic system. The main contents include modeling in the frequency and time domain, time response, reduction of multiple subsystems, stability, steady-state error, root locus technique, frequency response technique, and design via frequency response and state-space.
    (Prerequisite: EE202)

  • This course studies the representation, analysis, and design of discrete-time signals and systems. Topics include a review of the z-transform and the discrete Fourier transform, the fast Fourier transform, digital filter structures, digital filter design techniques, analog-to-digital, and digital-to-analog data conversion, rate conversion, sampling and aliasing issues. (Prerequisite: EE202)

  • 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)

     

  • This course teaches the principles of wireless network access techniques and system applications. The main focus of contents covers wireless medium access techniques, multiple access control and scheduling, system capacity optimization, and their applications to WiFi, WiMax, and ad-hoc sensor networks.

  • Two major themes of this course are 'Modern Control System' and 'Computational Intelligence'. Each lecture will address a balanced emphasis on the theory about the control system and its applications in practice. The first part of this course includes digital control system design and state-space methods for control system design. The basic system identification scheme will also be included, considering the control of unknown systems. Once background knowledge of the modern control system is established, this course will then focus on the second part composed of computational intelligence using fuzzy logic, artificial neural network, and evolutionary computation as main topics to introduce recent trend in intelligent control. Term projects will be assigned to test the algorithms to the given problems. (Prerequisites: EE381)

  • This course discusses the operational principles, analysis, modeling, and design of power conversion circuits in power electronics and carried out Spice simulations. (Prerequisite: EE202

Show List
메뉴닫기