For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| ADS5002 | Basic Statistics | 3 | 6 | Major | Master/Doctor | 1-8 | Applied Data Science | Korean | Yes |
| In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment, discuss generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, and organizing and commenting R code. | |||||||||
| ADS5004 | Data Analysis Language | 3 | 6 | Major | Master/Doctor | 1-8 | Applied Data Science | Korean | Yes |
| This course provides students with opportunities to develop skills and solve statistical problems using Python and R. Students learn about Python programs and how to use them for efficient data analysis. Understand the software installation and settings required in statistical programming environment, and general programming concepts. This course emphasizes data processing and basic statistical analysis. This course requires basic knowledge of basic statistics and does not require prior experience in basic computer programming. | |||||||||
| ADS5010 | Application of Linear Algebra | 3 | 6 | Major | Master/Doctor | 1-8 | Applied Data Science | Korean | Yes |
| Linear Algebra is the study of vector spaces and linear transformations on vector spaces. Techniques from Linear Algebra are also used in analytic geometry, engineering, physics, natural science, computer science, and the social sciences. Topics include the use and application of matrices in the solution of systems of linear equations, determinants, real n-dimensional vector spaces, abstract vector spaces and their axioms, linear independence, span and bases for vector spaces, linear transformations, eigenvalues and eigenvectors, matrix factorizations, and orthogonality. Computer explorations using MATLAB is an integral component of this course. | |||||||||
| ADS5016 | Natural Language Processing | 3 | 6 | Major | Master/Doctor | 1-8 | Applied Data Science | Korean | Yes |
| Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. There are a large variety of underlying tasks and machine learning models behind NLP applications. In this course students will learn to implement, train, debug, visualize and invent their own neural network models. The course provides a thorough introduction to cutting-edge research in deep learning applied to NLP. this course will cover word vector representations, window-based neural networks, recurrent neural networks, long-short-term-memory models, recursive neural networks, convolutional neural networks as well as some recent models involving a memory component. | |||||||||
| ADS5030 | Data Structure and Algorithm | 3 | 6 | Major | Master/Doctor | 1-4 | Applied Data Science | Korean | Yes |
| In this course, we will take some knowledges of data structure such as link lists, stacks, queues, and trees. And we can also get some theories of basic algorithm such as sorting, searching, and graph theory. The students should be needed the prerequisite about basic programming knowledge. This course covers the most essential contents of data structure and algorithm, and aims to raise individual competence to learn self-intensively. | |||||||||
| ADS5032 | Data Science Applications | 3 | 6 | Major | Master/Doctor | 1-8 | Applied Data Science | - | No |
| Learning how to apply Data Science in real applications is important. After understanding the fundamentals of Data Science, students will be introduced to various methods in applying data science in different domains or practical applications. Latest topics in Data Science will be introduced. | |||||||||
| ADS5034 | Computer Vision | 3 | 6 | Major | Master/Doctor | 1-8 | Applied Data Science | Korean | Yes |
| This course focuses in the study of theories for image analysis. The first part consists of Image formulation model, early processing, boundary detection, region growing and segmentation, motion detection, merging and introduction of morphology. The second part, we cover basic concepts of statistical model, dis- criminant function, decision boundary and rules and neural network for visual pattern recognition. | |||||||||
| CHS5001 | Intensive Finance Research Seminars with Top Scholars | 3 | 6 | Major | Master/Doctor | Challenge Semester | - | No | |
| The purpose of the course is to expose SKKU students to path-breaking works in the past, recent developments in financial theories, and newly documented empirical evidence published in top-tier journal articles. Invited scholars may also introduce students to their research areas by providing students with an in-depth review of their own work and other related papers. The course will equip SKKU students with the knowledge and methodological tools required to write scholarly articles that are potentially publishable in top-tier journals in finance and related fields. The course would offer SKKU students an opportunity to directly work with prominent scholars, potentially leading to high-quality papers that are publishable in top-tier journals. Through the course, invited scholars would share their personal experiences with SKKU students in generating new research ideas, executing those ideas, writing-up the manuscript, and interacting with journal editors and referees, providing students with an invaluable opportunity to learn how the whole process works. The invitation could also help promote scholarly collaborations between visiting scholars and SKKU faculty members in the area of common interests. | |||||||||
| CHS5003 | Social Simulation based on Agent-based Modeling | 3 | 6 | Major | Master/Doctor | Challenge Semester | - | No | |
| The real world system consists of environment and various agents which are some kinds of objects. Each agent decides and acts according to its own decision process, and the system shows complex behaviors through interactions between the components (environment and agents). The social simulation using agent-based modeling is used to mimic the social phenomena (behaviors from interactions between agents), and used in various fields such as transportation, public health, and national defense industry. This course aims to learn the concepts and examples of social simulation using agent-based modeling. focusing on basic probability and statistics, population synthesis, agent-based modeling methodology, and the epidemic simulation. | |||||||||
| 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. | |||||||||
| CHS7001 | Introduction to Blockchain | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | - | No | |
| This course deals with the basic concept for the overall understanding of the technology called 'blockchain'. We will discuss the purpose of technology and background where blockchain techology has emerged. This course aims to give you the opportunity to think about the limitations and applicability of the technology yourself. You will understand the pros and cons of the two major cryptocurrencies: Bitcoin and Ethereum. In addition, we will discuss the concepts and limitations about consensus algorithm (POW, POS), the scalability of the blockchain, and cryptoeconomics. You will advance your understanding of blockchain technogy through discussions among students about the direction and applicability of the technology. | |||||||||
| 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. | |||||||||