For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| ADS5006 | Advanced Machine Learning | 3 | 6 | Major | Master/Doctor | 1-8 | Applied Data Science | Korean | Yes |
| This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas. | |||||||||
| ADS5013 | Advanced in Database System | 3 | 6 | Major | Master/Doctor | 1-8 | Applied Data Science | Korean | Yes |
| From this course, students learn fundamental concept and theories of data management system (DBMS). This course introduces principal technique of DBMS, data load, external sort, tree indexing, hash indexing, query optimization, physical design and tuning, transaction, concurrency control, recovery techniques. | |||||||||
| 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. | |||||||||
| AIM5001 | Theories of Artificial Intelligence | 3 | 6 | Major | Master/Doctor | Artificial Intelligence | - | No | |
| In this course students will learn the fundamental algorithms of Aritificial Intelligence including the problem solving techniques, search algorithms, logical agents, knowledge representation, inference, and planning. After taking the course, students are expected to implement the algorithms using computer programming languages. | |||||||||
| AIM5004 | Deep Neural Networks | 3 | 6 | Major | Master/Doctor | Artificial Intelligence | - | No | |
| In this class, we will cover the following state-of-the-art deep learning techniques such as linear classification, feedforward deep neural networks (DNNs), various regularization and optimization for DNNs, convolutional neural networks (CNNs), recurrent neural networks (RNN), attention mechanism, generative deep models (VAE, GAN), visualization and explanation. | |||||||||
| 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. | |||||||||
| COG5034 | Understanding of software design | 3 | 6 | Major | Master/Doctor | Computer Science Education | - | No | |
| In this lecture, we learn advanced programming techniques using object-oriented and generalized programming languages so that they can be applied to practical and educational sites. Understand and utilize the basic concepts of object-oriented language, objects, classes, polymorphisms, inheritance, etc., and cultivate the ability to solve problems using object-oriented language. We also learn how to design software that operates in various environments through generalized programming techniques. | |||||||||
| CON4004 | Consumer&MarketAnalysis | 3 | 6 | Major | Bachelor/Master | Consumer Science | Korean | Yes | |
| This course provides a well-grounded understanding of consumer market and business strategies that contribute to consumer wellbeing as well as profitability of companies. Specifically, students implement the macro environmental analysis and major companies’ 4P(product, Price, Promotion, Place) analysis, and conduct consumer survey. Based on the results, students practice product development and establishment of marketing strategy. | |||||||||
| CON4005 | Product Anatomy and Consumer Studies | 3 | 6 | Major | Bachelor/Master | Consumer Science | Korean | Yes | |
| To develop capabilities as a product development and planning expert, students learn how to systematically decompose and analyze the components and attributes of products from various angles, and generate ideas for developing consumer-oriented new products. | |||||||||
| CON4010 | Prosumer and Platform Economy | 3 | 6 | Major | Bachelor/Master | Consumer Science | - | No | |
| Based on understanding the fundamentals of platform economy and the role of consumers as a provider and buyer, evaluate current platform economy, and pursue improvement of platform economy in terms of economic well-being of prosumers. | |||||||||
| CON4012 | Consumer Dispute | 3 | 6 | Major | Bachelor/Master | Consumer Science | Korean | Yes | |
| This course focuses on dispute resolution procedures, legal regulations, mediation, and negotiation skills from the perspective of consumer protection and rights enhancement. Its goal is to equip students with the ability to analyze consumer issues and propose practical solutions. Through this course, students will gain both theoretical knowledge and practical skills in consumer dispute resolution, fostering the capabilities needed to contribute to consumer empowerment. Additionally, they will develop communication and problem-solving skills essential for understanding and applying collaborative approaches in the dispute resolution process. | |||||||||
| CON5001 | Studies in Consumer Policy | 3 | 6 | Major | Master/Doctor | 1-4 | Consumer Science | - | No |
| valuation of consumer policies in Korea by understanding the principles and logics in intermediary roles of government and consumer policies in OECD countries. | |||||||||
| CON5004 | Quantitative Method for Consumer Research | 3 | 6 | Major | Master/Doctor | 1-4 | Consumer Science | Korean | Yes |
| study on research methods and statistical analysis for dissertations and researches in comsumer science. | |||||||||