Curriculum

The course begins with the quantum logic and aims to deliver how quantum advantages can be achieved in communication and computational tasks. Examples of quantum algorithms and quantum protocols are provided. Known approaches to implement quantum information processing are explained.

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)

Special topics in electrical engineering for new theoretical and applied fields will be covered in this lecture that involves a suitable subtopic(s).

Special topics in electrical engineering for new theoretical and applied fields will be covered in this lecture that involves a suitable subtopic(s).

In this course, we shall learn about ferroelectric materials, devices, and applications that have recently attracted great interest. Only students who have completed the physical electronics, device physics, and solid state physics prerequisite courses can take this course. Also we will accept only students who can participate in 6 hours of manufacturing and device evaluation experiments together with class per week.

In this course, we shall learn about ferroelectric materials, devices, and applications that have recently attracted great interest. Only students who have completed the physical electronics, device physics, and solid state physics prerequisite courses can take this course. Also we will accept only students who can participate in 6 hours of manufacturing and device evaluation experiments together with class per week.

Machine learning and artificial intelligence technology revolutionize how computers run cognitive tasks based on a massive volume of observed data. As more industries are adopting the technology, we are facing fast-growing demands for new types of hardware that enable faster and more energy efficient processing in relevant workloads. In this class, I will overview recent advances in machine learning models, especially on deep neural networks (DNNs), and discuss various hardware acceleration platforms and architectures from both academia and industry, where their application domain ranges from energy efficient mobile/edge to hyper-scale cloud infrastructure.

This course is designed for the third/fourth-year undergraduate (or even first-year graduate) students. The main focus of this course is to understand the mathematical description of robotics. The contents of this course will be relevant to any subfield of robotics such as planning, state estimation, manipulation, and robot learning. While this course is designed to be self-contained, it would be advantageous if you are familiar with control theory, linear algebra, differential equations, and Matlab/Simulink.

In addition to the security and privacy of our everyday life, uses of cryptography have been continuously expanding from quantum cryptography to blockchain/cryptocurrency. Instead of understanding detailed mathematical theories behind cryptography, the purpose of this class is to learn basic cryptography, cryptographic protocols, and the current and future applications of cryptography. As a case study, we will review details of the blockchains technology.

This course introduces the fundamentals of reinforcement learning in a way that is accessible to undergraduate students. It covers the minimum required mathematics for understanding reinforcement learning and helps students develop interest through simple examples and Python-based exercises. The course covers classical reinforcement learning topics such as Markov Decision Processes, dynamic programming, TD learning, and Q-learning, as well as recent advances in deep reinforcement learning.