Korea, South

This course explores the underlying principles of several cutting-edge topics in machine learning and deep learning, including adversarial attacks, deep metric learning, generative models, information theory, and reinforcement learning.  

In addition, the course examines the end-to-end construction of modern large language models and practices core concepts by implementing them. Students engage in coding assignments and team projects using GPU-enabled computer servers to test original ideas. 

Topics include concepts and history of deep learning, backpropagation techniques such as stochastic gradient descent, initialization techniques, regularization techniques such as drop out, convolutional neural networks (CNN), CNN architectures, visualization of CNN, recurrent neural networks (RNN), RNN applications, and other applications including reinforced learning. 

To emphasize practical skills to implement deep learning algorithms, programming-related lectures and lab sessions are included. The most important/popular language, Python, will be covered and a Python math library called Numpy is also taught with lab sessions. Advanced deep learning algorithms are implemented in Tensorflow library, which is introduced as well including relevant lab sessions 

This course covers various topics in probability theory and introductory random processes such as probability, random variables, expectations, characteristic functions, random vectors, random processes, correlation functions, and power spectrum. A number of engineering examples are examined for students’ better understanding of principles. 

This course compares communication phenomena of East Asian societies using student-led international discussions, group studies, and special lectures. Topics include understanding of Chinese, Japanese and Korean media, as well as comparing western and eastern media characteristics. 

This course challenges the limitations of border-based thinking about and explores diverse aspects of (East) Asian society, particularly Korea, Japan, China, and beyond, through the layers of histories, networks, and complex sociotechnical entanglements. Drawing from the methods and theories in Communication and Media Studies, Cultural Studies, Asian/Global Studies, and Science and Technology Studies (STS), the course takes a critical, historically informed, and locally grounded approach to examine both the material and immaterial layers constituting the location in question. Through this course, students reflect on their experiences and perceptions of Asia, practice synthesizing theory with practice, and produce contextualized knowledge about Asia.    

This course covers concepts and characteristics required to build and operate a smart factory to improve manufacturing competitiveness. Students first learn about the composition and functions of the 4th Industrial Revolution and smart factories, then examine the general concept of factory automation and learn about the basic components of factory automation such as control systems and PLCs.  The course covers functions of corporate information systems such as ERP, MES, APS, and PLM, and concepts of smart factory digital platforms and big data-based decision-making required for intelligent factory operation. 

Health psychology is the study of how biological, psychological, and social factors influence health, illness, and healthcare. This course aims to (1) understand the field of health psychology, (2) examine the biopsychosocial model in health promotion and risk behaviors, (3) integrate biological, psychological, and social approaches to prevention and treatment, and (4) analyze how behaviors and psychosocial factors like stress impact physical and mental health. Topics include The systems of the body, Stress, Coping, resilience and social support, Health behaviors, Health promoting behaviors, Health compromising behaviors, The management of pain and discomfort, Management of chronic health disorders, Psychological issues in terminal illness, Heart disease, hypertension, stroke, and Type II Diabetes, Psychoneuroimmunology and immune-related disorders, and Using health services / Patients, providers, and treatments. 

This course covers the application of management science methodology to various decision-making problems in the agricultural and food industry, models them mathematically, and derives optimal strategies. The course enables students to actually apply and discuss management science methodology to corporate cases related to the agricultural and food industry. Specifically, in this course, students acquire theoretical models for linear programming, network models, integer programming, nonlinear programming, multi-objective decision-making, decision-making under uncertainty, simulation, and queueing, and learn how to analyze them. In addition, it enables students to apply management science methodology to topics such as agricultural cultivation, food distribution and supply chain, and agricultural and food production planning, and derive and interpret results. 

This course covers the theory of marketing using the Internet and cultivates practical application skills. Students examine theories on Internet marketing and e-commerce, exploring the unique aspects and challenges inherent in marketing driven by the Internet. Students develop the ability to think critically and strategically about opportunities and issues that emerge in marketing driven by the Internet and to formulate valuable solutions.  

Students should be aware of the unstructured style of this course. No textbook is required, and only a handful of lecture slides are distributed. The class consists of a mixture of short lectures, student discussions of assigned materials, case discussions, team presentations, and active learning exercises. There are also class visits by service practitioners/experts in which they share insights.  

Pre-requisites: Marketing management (BUSS205) or equivalent courses; Statistical analysis: correlation, regression analysis. 

This course provides an opportunity to learn through practice of combined fundamental mathematics and programming to understand machine learning. The course operates as micro-learning that allows students to learn the necessary unit concept of mathematics and learn through programming exercises immediately. This course covers the essential requirements for machine learning such as algebra, calculus, linear algebra, and geometry. The programming language used in this course is Python. This course is mainly targeted for undergraduate students with advanced high-school level mathematics but with no background in programming. Some basic machine learning algorithms will be introduced to show the application of mathematics in practice. Finally, some advanced learning algorithms and important topics will be reviewed.   

This course covers basic design theories and form construction techniques required for three-dimensional forms and functions. Topics include basic shape elements and principles that consist of form and shape, and practical methodology of creating form, function and aesthetics. Students acquire basic form-giving abilities as industrial designers by learning design materials and the characteristics of form composition. 

This physical activity course covers rock/sport climbing at the beginner level.