Curriculum

This course explores the design of intelligent robot behaviors using modern artificial intelligence techniques. Students will work with a provided robot platform equipped with ROS 1/2, SLAM, a vision-based robotic arm, and multimodal input capabilities. The course begins with foundational knowledge in ROS and SLAM, followed by robot kinematic manipulation and path planning. In the latter half, students will learn to integrate machine learning techniques—such as object detection, large language models (LLMs), and reinforcement learning (RL)—to enable perception-driven, interactive, and goal-directed robotic behaviors. The course culminates in a final team project where students design and implement an intelligent robotic system capable of performing real-world tasks.

• This course introduces social innovators who directly address societal issues through their work, and analyzes technologies to propose solutions that can further expand the impact of their efforts.
• Link to the previous project topics: https://techforimpact.io/campus/project
• It features stories and case studies of “Brian Fellows” who have founded nonprofit organizations and are creating change across various fields such as the environment, diversity, health, and politics. Based on these, students will work on assignments that propose technological solutions to help innovators solve their challenges more quickly and effectively.
• During the first weekend of September (Friday–Saturday, 1 night and 2 days), students will form teams and define problems at the Kakao AI Campus, receiving guidance from the fellows, and will prepare a project proposal.
• A modest activity fund will be provided to support user interviews and necessary tools for the course. When needed, Kakao developers can offer technical mentoring and feedback.
• All students who complete the course will receive a certificate of completion, and top projects or students may be awarded prizes and follow-up project support.
• This semester places greater emphasis on identifying social problems and planning technological solutions.
• Class Time: Every Friday, 16:00 – 19:00

This course provides theory and technique for design and analysis of combinational/sequential digital circuits using discrete mathematics. Topics include: basics for set, relation and lattices; switching and boolean algebra, and switching function; combinational logic synthesis by functional decomposition; fault detection in combinational / sequential circuits; structure of finite state automata; automata-to-machine transformation; state and machine identification; properties of finite state machine with memory span; inverse machine; communicating finite state machine and systems verification; binary decision diagram and its application. (Prerequisite: EE303)

We Study introductory mathematical and programming tools for big data analytics, in particular focusing on recently successful real-world applications, e.g., web search, spam filtering, crowd-sourcing, visualization, and recommendation system

In this lecture, various hardware and software components and system implementation aspects of an embedded system are covered. Covered topics include bus-based expandable ARM processor-based board, open-source embedded Linux operating system, PC-based software development environment, digital and analog interface techniques, ARM assembly language, device drivers. Hands-on experience is gained to enhance firm understanding.
(Prerequisite: EE303)

This course provides students with the knowledge and skills necessary to build a foundation in system programmings for Electrical Engineering, especially focused on operating systems and implementation. Topics include an overview of the components of an OS, concurrency, synchronization, processes, memory management, I/O devices, and file systems.

This course emphasizes practical implementation aspects of digital communication systems. A physical-layer software implementation project will be assigned for a selected commercially-deployed communication system. Topics covered in this digital communication course include (1) Digital modulation and demodulation, Optimum receivers, (2) Adaptive equalization and Synchronization, (3) Channel capacity, Error control codes.
(Prerequisite: EE321)

The primary objective of this course is to present fundamental concepts and basic techniques of optimization with possible applications, which are essential for researches in circuit design, communications, signal processing, and control engineering. Topics include linear vector spaces and linear operators, linear estimation and filtering, functional analysis, optimal control, linear programming, nonlinear programming, dynamic programming, genetic programming (evolutionary computation), and neural networks.

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.

As more industries are adopting artificial intelligence (AI) and machine learning (ML) 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, we will overview recent advances in AI/ML models, and study various AI silicon systems from both academia and industry