Curriculum

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.

This course will survey key concepts of quantum mechanics, solid-state physics, and semiconductor physics in view of realizing nano/quantum electronic devices. Rather than the traditional approach to semiconductor devices based on the drift-diffusion equation, the first-principles approach starting from quantum transport theory will be presented.

This course covers machine learning techniques to analyze visual data. Specifically, this course focuses on fundamental machine learning and recent deep learning methods that are widely used in visual data analysis, and discusses how these methods are applied to solve various problems with visual data. This course consists of lectures, practices, and projects.

System software is a fundamental driving force that lets the applications to interact with the computer hardware. The students will learn the role, the internal design and implementation of the system software including shell, linking, loading and operating system internals.As a reference operating system, we will use xv6 and Linux.

This course covers variety of audio processing techniques for Virtual Reality (VR), 3D Audio, Room Impulse Responses, Basic Filter Design, and Sound Source Localization. Basic principles of sound propagation and human hearing are explained with listening examples. Applications and exemplary implementation of individual topics are presented with Matlab codes. Single channel filtering, time-frequency analysis, multichannel signal processing are major tools utilized for these applications. This course also offers term projects in which students can experience one of these techniques by their own. The course is designed to practice the knowledge learned from Signals and Systems & Digital Signal Processing.
Main text: lecture slides, Prerequisites: EE202 Signals & Systems