For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| CHS5005 | AI Startup and Entrepreneurship | 3 | 6 | Major | Master/Doctor | 1-4 | Challenge Semester | - | No |
| Recent years have witnessed a rapid increase in the number of so-called AI startups with AI as their core value, as the scope of AI's application across all industries has expanded significantly. This is gaining popularity not only in Korea, but globally as well. However, there are no theoretical or empirical guidelines regarding the entrepreneurial skills and business models that AI startups in a hypercompetitive market should possess. It is extremely harsh for those AI startups that are actually traditional businesses dressed up to look like they use AI to to succeed in a very competitive market. For AI startups with inadequate business acumen, gaining a foothold on the market is also a daunting task. By focusing on the following three goals, henceforth, this course aims to assist the growing number of AI startups with their challenges. Firstly, it categorizes the various possible business models for AI startup companies. Secondly, it then examines some of the most prominent domestic and international cases to illustrate the various types of entrepreneurship that AI startups require to thrive. Thirdly, a hypothetical AI startup is created, on a team basis, using real-world software such as Landbot, Stable Diffusion, and a number of no-code ML/DL (machhine learning/deep learning). Then its business model and entrepreneurship are established; and its efficacy is evaluated. | |||||||||
| CHS7002 | Machine Learning and Deep Learning | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | - | No | |
| This course covers the basic machine learning algorithms and practices. The algorithms in the lectures include linear classification, linear regression, decision trees, support vector machines, multilayer perceptrons, and convolutional neural networks, and related python pratices are also provided. It is expected for students to have basic knowledge on calculus, linear algebra, probability and statistics, and python literacy. | |||||||||
| CHS7003 | Artificial Intelligence Application | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | - | No | |
| Cs231n, an open course at Stanford University, is one of the most popular open courses on image recognition and deep learning. This class uses the MOOC content which is cs231n of Stanford University with a flipped class way. This class requires basic undergraduate knowledge of mathematics (linear algebra, calculus, probability/statistics) and basic Python-based coding skills. The specific progress and activities of the class are as follows. 1) Listening to On-line Lectures (led by learners) 2) On-line lecture (English) Organize individual notes about what you listen to 3) On-line lecture (English) QnA discussion about what was listened to (learned by the learner) 4) QnA-based Instructor-led Off-line Lecture (Korean) Lecturer 5) Team Supplementary Presentation (Learner-led) For each topic, learn using the above mentioned steps from 1) to 5). The grades are absolute based on each activity, assignment, midterm exam and final project. Class contents are as follows. - Introduction Image Classification Loss Function & Optimization (Assignment # 1) - Introduction to Neural Networks - Convolutional Neural Networks (Assignment # 2) - Training Neural Networks - Deep Learning Hardware and Software - CNN Architectures-Recurrent Neural Networks (Assignment # 3) - Detection and Segmentation - Generative Models - Visualizing and Understanding - Deep Reinforcement Learning - Final Project. This class will cover the deep learning method related to image recognitio | |||||||||
| CHS7004 | Thesis writing in humanities and social sciences using Python | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | - | No | |
| This course is to write a thesis in humanities and social science field using Python. This course is for writing thesis using big data for research in the humanities and social sciences. Basically, students will learn how to write a thesis, and implement a program in Python as a research methodology for thesis. Students will learn how to write thesis using Python, which is the most suitable for processing humanities and social science related materials among programming languages and has excellent data visualization. Basic research methodology for thesis writing will be covered first as theoretical lectures. Methodology for selection of topics will be discussed also. Once a topic is selected, a lecture on how to organize related research will be conducted. In the next step, students learn how to write necessary content according to the research methodology. Then how to suggest further discussion along with how to organize bibliography to complete a theoretical approach. The basic Python grammar is covered for data analysis using Python, and the process for input data processing is conducted. After learning how to install and use the required Python package in each research field, the actual data processing will be practiced. To prepare for the joint research, learn how to use the jupyter notebook as the basic environment. Learn how to use matplolib for data visualization and how to use pandas for big data processing. | |||||||||
| COV7001 | Academic Writing and Research Ethics 1 | 1 | 2 | Major | Master/Doctor | SKKU Institute for Convergence | Korean | Yes | |
| 1) Learn the basic structure of academic paper writing, and obtain the ability to compose academic paper writing. 2) Learn the skills to express scientific data in English and to be able to sumit research paper in the international journals. 3) Learn research ethics in conducting science and writing academic papers. | |||||||||
| ERP4001 | Creative Group Study | 3 | 6 | Major | Bachelor/Master | - | No | ||
| This course cultivates and supports research partnerships between our undergraduates and faculty. It offers the chance to work on cutting edge research—whether you join established research projects or pursue your own ideas. Undergraduates participate in each phase of standard research activity: developing research plans, writing proposals, conducting research, analyzing data and presenting research results in oral and written form. Projects can last for an entire semester, and many continue for a year or more. SKKU students use their CGS(Creative Group Study) experiences to become familiar with the faculty, learn about potential majors, and investigate areas of interest. They gain practical skills and knowledge they eventually apply to careers after graduation or as graduate students. | |||||||||
| FSE5003 | Social Economy & Social Entrepreneurship | 3 | 6 | Major | Master/Doctor | 1-4 | Social Entrepreneurship and Humanistic Future Studies | Korean | Yes |
| The main area of activity of the social entrepreneur is the social economy. Social economy is one of organizing forms and ways of economic activities in comtemporary societies but it has received the least systematic treatment by academics and social scientists. The goal of this course is to provide students with a comprehensive understanding of the social economy, the core activity area of the social entrepreneur, by addressing some important topics of social economy. | |||||||||
| GSP5241 | Spatial Modeling for Social Science Research | 3 | 6 | Major | Master/Doctor | Public Administration | - | No | |
| Spatial dependence is prevalent in the society. Geographically proximate individuals, groups, and localities have similar characteristics and behave similarly via spillover effect. Also, the local governments that are closely located often pursue similar policy directions as they are affected by each other. This class aims to explore how to apply spatial dependence in social science research. The class topics include the concept and origin of spatial dependence, global/local spatial analysis, visualization of spatial dependence, and various spatial regression models such as spatial lag, spatial error, geographically weighted regression, and spatial Durbin. | |||||||||
| MCJ5122 | Network Analysis for Communication Research | 3 | 6 | Major | Master/Doctor | 1-4 | Media and Communication | Korean | Yes |
| The primary goal of this course is to explore theories and concepts of network analysis in communication perspectives. Moreover, this course will practice with data and network analysis programs to show how network analysis can be used to understand various communication phenomena and problems. | |||||||||
| MCJ5128 | Meta-Analysis | 3 | 6 | Major | Master/Doctor | 2-4 | Media and Communication | - | No |
| Meta-analysis refers to the quantitative analysis of study outcomes. Meta-analysis consists of a collection of techniques that attempt to analyze and integrate effect sizes (indices of the association between an independent variable and a dependent variable) that accrue from research studies. This course deals with the process of performing meta-analysis and how to interpret analysis results. Students will have an opportunity to conduct meta-analysis on research topics of interest using meta-analysis software and to write a research paper based on the analysis results. | |||||||||
| PSD5113 | Future Social Risks and Political Theory | 3 | 6 | Major | Master/Doctor | Political Science | Korean | Yes | |
| This course diescusses the new developments in future, the risks they bring about and proper strategies for coping with them. The topics include the climate changes, natural disastors, demographic changes, new discoveries and developments in sciences and technologies and their impacts on our society. The goal of this course is to deliberate on how to react to those changes and challenges properly, which may determine the tufure of individual societies as well humankind. | |||||||||
| PSD5120 | Data Science and Study in Political Science) | 3 | 6 | Major | Master/Doctor | 1-4 | Political Science | - | No |
| The scientific study of political science has been one of the most noted fields that so-called `big data revolution’ and a rapid development in computational approaches have changed the way we understand our world. This course aims at introducing fundamental techniques of data science from a social science perspective, with a focus on political science. In this course, we will discuss a set of cutting-edge methods related to data science, including text analysis/mining, network analysis, machine learning, information extraction methods and data visualization techniques. We will then shift to an in-depth discussion on how to apply them to various research topics in politics such as political behavior, the roles of media and interest groups, and more. Students will also learn basic data analysis tools using R programming language or Python through problem sets. | |||||||||
| PSY4007 | Multivariate analysis and statistical learning | 3 | 6 | Major | Bachelor/Master | Psychology | - | No | |
| This course covers principles and practice of multivariate data analysis in psychology and related fields. Applications of multiple regression, logistic regression, principal component analysis, factor analysis, cluster analysis and other multivariate techniques for psychological research will be illustrated. In addition, supervised learning and unsupervised learning will be discussed in relation to the multivariate techniques. This course is designed for graduate students or 3rd- or 4th-year undergraduate students in psychology. | |||||||||
| PSY5136 | Structural Equation Modeling and Related Methods | 3 | 6 | Major | Master/Doctor | 1-4 | Psychology | - | No |
| This course is an introduction to structural equation modeling(SEM) which is quite broad and deep as a multivariate method. Students are required to understand the basic theory of SEM and to analyze multivariate data with hypotheses of linear structural relations. Also they are trained to be able to understand published articles demonstrating use of SEM as the major analytic method. | |||||||||
| PSY5188 | Structural Equation Modeling | 3 | 6 | Major | Master/Doctor | Psychology | Korean | Yes | |
| This course covers fundamentals of latent variable modeling, path analysis, and structural equation modeling, combining theoretical and practical perspectives. The course is designed to provide details of structural equation modeling, from the statistical underpinnings to how to conduct various types of structural equation analyses. | |||||||||