Seoul National University

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 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. 

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 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 cultivates entrepreneurship through understanding the current status and trends of the semiconductor industry and the experiences of company founders. In the class, students meet representatives of recently established semiconductor companies, hear about the startup motivation, business model, and future prospects, and share understanding of the overall semiconductor industry and various experiences related to technology-based startups through a Q&A session with the semiconductor CEO. 

This course covers estimating relationships between economic variables associated with agricultural situations. Students are enabled to understand the general concepts about model identification, estimation, forecasting, and policy analysis. Students learn simple regression, multiple regression, and time series analysis. Prerequisite: Principles of Economy, Statistics, Mathematics for Economic Analysis. 

This class is designed for international students studying at SNU, in particular those students who may not have had the opportunity yet to study the language or culture of Korea. It provides a general introduction to the Korean language and important aspects of Korean culture, both traditional and modern. Topics include an outline of the Korean language with honorific forms, Korean history, nature, economy, and society. The course also covers Korean art, music, literature and philosophy as well as problems concerning the traditional culture such as family, relatives, wedding, funeral ceremony, folk's belief, shamanism, seasonal rite and customs. The class is conducted in English, including all instruction, discussion, and assignments. 

This course focuses on the food industry and studies the success and failure cases of marketing and information management in fields such as agriculture, food service, bio industry, and distribution industry, and discusses how to apply them for the development of our food industry. Through case analysis, students will acquire various practical knowledge on how business activities in these industries are developed from the perspective of marketing and information management, and how to solve problems using methods and frameworks. Students who successfully complete this course will have basic skills as management consultants in the field of food and bio business. 

This course introduces population geography to undergraduate students and focuses on the causes and consequences of population change. It enables students to understand demographic dynamics brought about by birth, death, and mobility. The course examines the tension between how demographic knowledge (and in particular, demographic categories) has been constructed and how such categories are used. The course pays special attention to the spatial mobility of human beings as the increase in human mobility receives increasing attention from both academia and policy-making.