For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| BUS2007 | Investments | 3 | 6 | Major | Bachelor | 1-4 | Business Administration | Korean,English | Yes |
| This course is designed to provide the students with an understanding of our financial markets, financial instruments, basic valuation principles and systematic investment management. Topics include operations of financial markets, analysis of financial instruments, such as stocks, bonds, options and futures, various model of the capital asset prices, and investment strategies. | |||||||||
| 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 | |||||||||
| 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 | |||||||||
| 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. | |||||||||
| ECO3031 | Financial Econometrics | 3 | 6 | Major | Bachelor | 3-4 | Economics | Korean | Yes |
| This course introduces basic concepts and techniques for financial time series analysis. This course introduces major econometric models used for financial time series and the inferential procedures for these models. The econometric models that will be discussed are linear regression, autoregressive moving average (ARMA), autoregressive conditional heteroskedasticity (ARCH) and vector autoregressive (VAR) models, and stochastic volatility models. Asset price preditability and major asset pricing theories like capital asset pricing model and arbitrage pricing theory will be covered from an empirical viewpoint. | |||||||||
| 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. | |||||||||
| FIT5003 | Financial Statistics | 3 | 6 | Major | Master/Doctor | FinTech | Korean | Yes | |
| This course covers an introductory level of probability and statistical analysis for graduate students who major in finance. We emphasize topics of probability and statistical theories that students will encounter in graduate finance and econometric courses. Topics include probability theory, sampling, statistical estimation, and hypothesis testing. | |||||||||
| FIT5005 | AI & Wealth Management | 3 | 6 | Major | Master/Doctor | FinTech | - | No | |
| This is a course in AI & Wealth Management for FinTech Master or Ph.D. program.This is a course in AI & Wealth Management for FinTech Master or Ph.D. program.This is a course in AI & Wealth Management for FinTech Master or Ph.D. program.This is a course in AI & Wealth Management for FinTech Master or Ph.D. program.This is a course in AI & Wealth Management for FinTech Master or Ph.D. program.This is a course in AI & Wealth Management for FinTech Master or Ph.D. program.This is a course in AI & Wealth Management for FinTech Master or Ph.D. program. | |||||||||
| FIT5006 | Blockchain & Financial Application | 3 | 6 | Major | Master/Doctor | FinTech | Korean | Yes | |
| This is a course in Blockchain & Financial Application for FinTech Master or Ph.D. program.This is a course in Blockchain & Financial Application for FinTech Master or Ph.D. program.This is a course in Blockchain & Financial Application for FinTech Master or Ph.D. program.This is a course in Blockchain & Financial Application for FinTech Master or Ph.D. program. | |||||||||
| ISS3233 | Statistics in Python | 3 | 6 | Major | Bachelor | 1-4 | English | Yes | |
| This course will cover elementary topics in statistics using Python. The statistics topics include principles of sampling, descriptive statistics, binomial and normal distributions, sampling distributions, point and confidence interval estimation, hypothesis testing, two sample inference, linear regression, and categorical data analysis. Using Python, students will learn basic knowledge in Python programming, data management, data formats and types, statistical graphics and exploratory data analysis, and basic functions for statistical modeling and inference. | |||||||||
| ISS3290 | Introduction to Big Data Analysis | 3 | 6 | Major | Bachelor | English | Yes | ||
| Understand the genesis of Big Data Systems • Understand practical knowledge of Big Data Analysis using Hive, Pig, Sqoop • Provide the student with a detailed understanding of effective behavioral and technical techniques in Cloud Computing on Big Data • Demonstrate knowledge of Big Data in industry and its Architecture • Learn data analysis, modeling and visualization in Big Data systems | |||||||||
| MAE2007 | Analysis I | 3 | 6 | Major | Bachelor | 2-3 | Mathematics Education | Korean | Yes |
| The main contents are real and complex number systems, limits of sequences and functions, continuity and differentiability of functions, Riemann integrability of functions. | |||||||||
| MAE2008 | Analysis Ⅱ | 3 | 6 | Major | Bachelor | 2-3 | Mathematics Education | Korean | Yes |
| The main contents of this course are sequences and series of functions, uniform convergence, differnetiation and integration of functions of several variabels, implicit function theorem, inverse function theorem, metric spaces. | |||||||||
| MAE3013 | Real Analysis | 3 | 6 | Major | Bachelor | 4 | Mathematics Education | Korean | Yes |
| In this course, we study the Lebesque measure and theory of integration. -Measure Theory and Integration : albebra, sigma-algebra, measure, integration, convergece, L^p-space | |||||||||
| SOE3001 | Statistical Learning and Artificial Intelligence | 3 | 6 | Major | Bachelor | 3-4 | Economics | - | No |
| This course covers a variety of topics related to statistical learning and artificial intelligence. Students will learn concepts and basic theories of various statistical methods and models used in artificial intelligence. In order to cultivate analytical skills and problem-solving skills for real data, Python program and deep learning package are used to practice real data. | |||||||||