The Global Leader, SKKU

Search
Close
search
 

Undergraduate

  • home
  • Undergraduate
  • Social Welfare
  • Course & Curriculum

Course & Curriculum

For more details on the courses, please refer to the Course Catalog

교육과정
Code Course Title Credit Learning Time Division Degree Grade Note Language Availability
CHS2009 Creative Ideation 2 4 Major Bachelor 1-4 Challenge Semester - No
Most people think that creativity is closely related to something new, unique and original. But we have no idea how to do if we actually think up creative ideas, which has never been existed, on our own. Let's take note of the well-known old saying, there is nothing new under the sun. We should change our perspective on creativity. There is common and distinct patterns in those things considered to be creative. This course introduce the common patterns of creative ideation with a lot of examples. Major topics include systematic inventive thinking, creative ideation codes, biomimicry, creativity in culture and arts.
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.
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
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.
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.
CON2012 Foundation of Consumer Bigdata Analysis1 3 6 Major Bachelor 2-3 Consumer Science Korean Yes
Learn and practice to collect, store, and analyze naturally-generated consumer data using R program.
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.
ECO2003 Microeconomics 3 6 Major Bachelor 2-3 Economics Korean,English,Korean Yes
A detailed examination of micro aspects of economic theory at the intermediate level. Topics discussed are theories of consumer behavior and demand, productionand cost, organization of the firm and the market, distribution of factor income, general equilibrium, economic welfare and other current microeconomic issues.
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.
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.
ISS3178 Algorithm and Problem Solving 3 6 Major Bachelor - No
This course covers the fundamental of computing and computational thinking techniques, including the basics of how to construct computer programs using sequences of logical instructions. No prior computer science knowledge is required, but we expect students with moderate computer experience and understanding of rudimentary mathematics and logic. Emphasis will be placed on implementing computer programs towards solutions for practical problems using Python. The objectives of this course are:  to familiarize students with fundamental concepts of computing systems;  to facilitate students with ability to identify, formulate and solve computational engineering problems;  to develop students’ ability to develop computer programs and to read code developed by others; and  to provide students with hands-on experience in computer programing and computational problem solving.
ISS3183 Human Computer Interaction 3 6 Major Bachelor - No
This course covers the basic concepts, fundamental theories and current researches in humancomputer interaction. Topics include principles, theories, methodologies, design, implementation, evaluation and research in computer interfaces. The objectives of this course are:  to familiarize students with basic concepts of human computer interaction;  to introduce students to theories and principles in computer interface design;  to develop students’ ability to design, conduct and analyze user studies for computer software; and  to provide students with the knowledge of the design process for user interfaces.
ISS3200 Essential research methods for social science 3 6 Major Bachelor - No
What makes people happy? Why do some people drink too much? What motivates people to buy and students to study? How should our society address poverty? They are just a few of the questions that social science researchers with good knowledge and skills will be more than happy to tackle and answer. This course is designed to provide a foundation of such research skills and methodologies for all major social science fields, such as psychology, sociology, business, economics, anthropology, education, social work, political science, and communication. Students from other science fields, such as natural science, life science, engineering, medicine, pharmacy, and nursing, also will find this course relevant to the basic research knowledge and skills required in their respective field. The course will address main concepts and methods in social science research, such as research ethics, literature review, various types of research designs (survey, experiment, interview, focus group, observation, etc.), measurement, sampling, and data collection. No prior knowledge of advanced mathematics and statistics are needed. This course will survey many of these areas through fun-filled class activities rather than “boring” lectures.