• XAI630-Meta Learning This course covers meta-learning, one of the core areas of research in general artificial intelligence. Through this course, students will learn the fundamental algorithms of meta-learning and gain insights into various concepts such as multi-task learning, transfer learning, continual learning, and in-context learning.  

• XAI629-Intellignet Mobility Students learn about high-precision map and location estimation systems using GPS, cameras, LiDAR, and RADAR. In addition, knowledge about the route planning system for moving to the destination is acquired. Learn the principles of various element technologies that make up intelligent mobility, such as object recognition and tracking, precision maps, positioning, route planning, and vehicle control, and understand them through artificial intelligence. As a general overview of the composition and operation of intelligent mobility, it identifies the outline and technology of intelligent mobility and the trend of artificial intelligence technology.    

• XAI628-Digital Twin The main purpose and educational goal of the Digital Twin course is to foster a deep understanding of the principles and applications of Digital Twin technology among students, and to enhance their ability to solve complex problems in real-world industries as the importance of this technology is increasingly recognized in modern industrial society. This course will focus on bridging the gap between theoretical understanding and practical implementation of Digital Twins. Students will learn to virtualize real systems based on the theory, and to create and utilize digital twin models for performance improvement, diagnosis, and prediction. In doing so, this course will play a role in helping students gain practical experience and knowledge in solving real-world problems through Digital Twin technology.  

• XAI627-Advanced Generative Artificial Intelligence This course provides a deep understanding of the latest generative AI techniques and algorithms, understands the limitations of generative models, and discusses recent research that has been done to improve them.  

• XAI519-Generative Artificial Intelligence This course covers the fundamentals of various generative models used to generate diverse data formats and discusses the core concepts and applications of generative AI.  

• XAI518-Information Theory This course introduces fundamental concepts in information theory and their applications in machine learning, including several generative learning methods based on information theory.

• XAI626-Advanced Reinforcement Learning This course aims to learn theories and efficient algorithms in various problem settings based on fundamental knowledge of reinforcement learning. The course includes discussions on the latest reinforcement learning research trends.

• XAI625-Reinforcement Learning Application and Practice This course focuses on solving practical problems with fundamental knowledge in reinforcement learning. The course includes practical exercises to solve real world problems related to reinforcement learning.

• XAI550-Research Guidance Research guidance is given from the advisor for conducting competitive research in artificial intelligence.

• XAI712-Artificial Intelligence Colloquium This course consists of seminars covering recent academic and industry landscapes in artificial intelligence.