Curriculum

This course covers topics of interest in electrical engineering at the undergraduate level. The course content is specifically designed by the instructor.

• [2021 Spring] Philosophical Issues in AI (Subtitle No.042)
• [2021 Fall] Quantum and Solid-State Physics for Semiconductor Devices(Subtitle No.022)
• [2021 Fall] MyEE Leadership (Subtitle No.005)
• [2022 Spring] Ferroelectric devices and memory application (Subtitle No.001)
• [2022 Spring] Fundamentals on Robot control (Subtitle No.003)
• [2022 Spring] Introduction to cryptography engineering (Subtitle No.004)
• [2022 Spring] Hardware acceleration for machine learning (Subtitle No.002)
• [2022 Spring] AI ConvergenceCapston Design (Subtitle No.041)
• [2022 Fall] Design and Understanding of System Software (Subtitle No.032)
• [2022 Fall] Deep Learning for Computer Vision (Subtitle No.031)
• [2023 Spring] AI Convergence Capstone Design (Subtitle No.041)
• [2023 Spring] Introduction to Reinforcement Learning (Subtitle No.006)
• [2023 Fall] Deep Learning for Computer Vision (Subtitle No.031)
• [2023 Fall] Introduction to Integrated Photonics (Subtitle No.053)
• [2023 Fall] Modern Semiconductor Device Physics (Subtitle No.058)
• [2023 Fall] Learning Patterns for Autonomous Control (Subtitle No.043)
• [2024 Spring] Ferroelectric devices and memory application (Subtitle No.001)
• [2024 Spring] Introduction to Audio Signal Processing (Subtitle No.036)
• [2024 Spring] Introduction to Cybersecurity for EE (Subtitle No.060)
• [2024 Spring] Interactive Wearable Computing Lab (Subtitle No.061)
• [2024 Fall] Introduction to Integrated Photonics (Subtitle No.053)
• [2024 Fall] Deep Learning for Computer Vision (Subtitle No.031)
• [2024 Fall] Ultra Large Scale Quantum Computing Models and Engineering Applications (Subtitle No.065)
• [2025 Spring] Introduction to cryptography engineering (Subtitle No.004)
• [2025 Spring] Fundamentals on Robot control (Subtitle No.066)
• [2025 Spring] AI ConvergenceCapston Design (Subtitle No.041)
• [2025 Spring] AI in Cyber and Cognitive Strategies (Subtitle No.067)
• [2025 Spring] Display Devices Engineering (Subtitle No.068)
• [2025 Spring] Ferroelectric devices and memory application (Subtitle No.001)
• [2025 Spring] Mathematical Foundations of Social Sciences (Subtitle No.070)

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.

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 helps EE students develop leadership and personality cultivation by providing a new coursework consisting of lectures, book reading, group discussion, a field trip and the community service.

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.