KOREAN
CHINESE
Master’s in Artificial Intelligence
Master’s in Artificial Intelligence
The continued growth and importance of Artificial Intelligence (AI) on the future of technology has created a strong demand for qualified AI specialists globally. Our Master of Engineering in Artificial Intelligence looks to expand your knowledge of technology and AI so you can take part in the opportunities available in this field and in Industry4.0. Being a part of this now can ensure your position in the future as technology continues to become a more integral part of our lives. Considering the current environment, our AI Master’s degree program will be a core asset of the Woosong Graduate School.
Overview
- IntakeSpring & Fall
- Duration2 years
- Credits27
- TypeFull-time
- FormatOffline
- LanguageEnglish
What makes our program unique?
South Korea is already at the forefront of modern technology and it is because of embracing new ideas that has enabled Korea to maintain this position. Our program is in line with this thinking, we want to be at the forefront of technology and being located in Daejeon, Korea’s Silicon Valley, allows for us to create graduates that will do just that. Our students also be able to take full advantage of Woosong University’s 4Lab Research Institute, which was created to enhance industry application-based research capability in AI and big data.
Structure
The Master’s program is divided into three parts: Core, Major Required, and Major Elective. The degree may be completed within two years and has an optional thesis component. Those electing the non-thesis option must complete 27 credits and a comprehensive exam to graduate. Students who elect the thesis option are required to complete 24 credits and a thesis course; see the credit breakdown below.
| Credit Classification |
Thesis | Non-thesis |
|---|---|---|
| Core | 9 | 9 |
| Major Required | 6 | 6 |
| Major Elective | 3 | 6 |
| General Elective* | 6 | 6 |
| Thesis |
* General Elective credits are any additional credits that can be earned in any category (Core, Major Required, or Major Elective)
Students are required to achieve a cumulative GPA of 3.0 or higher to graduate.
2022 Curriculum
Core Courses
| Study Area | Course | Credits |
|---|---|---|
| Core | Advanced Software ▼
Course Description
This course introduces a special programming language for operating a computer system and a new high-level language. It studies the requirements for developing a new software system, and trains the techniques of processing languages of next-generation computers. |
3 |
| Topics in Culture Technology ▼
Course Description
Based on the digital media, this course teaches the technology of the culture and art industry such as film, broadcasting, game, and animation and studies the technology related to digitization of cultural industry with the main subject of cultural contents field. Understanding programming and graphic fundamentals as a base technology, planning and creating content, developing, producing, discussing the whole process of loading and delivering on media, and discussing the actual issue of virtual reality contents making or expressing culture technology. |
3 | |
| Critical Approaches to Modern Society & Design ▼
Course Description
For a long time, design has undergone many technological, methodological, conceptual, morphological, and stylistic changes, but the nature involved in human life has not changed much. Design is a combination of broad information and visual / art-linguistic communication that gives the identity. It also deals with social systems, political-economic interests, desires, and always leads us to the way of life, in the form of huge industrial and cultural symbols, sometimes trivial routines and materials. Design is a human creative activity to achieve the goal, and is a crystal of civilization and culture. Design is an entity and a measure of culture that defines the relationship between beauty and function, technology and artistry, tools and the environment in every civilization. Especially in modern society, individualism and multi-culturalism designers who live in modern times need to understand the creations of other cultures. Communication with the public is divided into culture, fashion, trend, mega trend, and the visual communication phenomenon. |
3 |
Major Required
| Study Area | Course | Credits |
|---|---|---|
| Major Required | Research Methods in Social Science ▼
Course Description
This course will introduce students to the premier method of empirical research in cultural anthropology: participant observation, and associated informal dialogue and interviewing. We will study techniques for planning and carrying out such research, and for recording, checking validity and reliability, storing, coding, analyzing and writing up of ethnographic data. Students will undertake "mini" research projects, and become familiar with basic ethical issues, informed consent, writing of research proposals, formulating research contracts, and sharing results with cooperating individuals and groups. |
3 |
| Quantitative Methodology ▼
Course Description
This course introduces several research methods frequently used in social science research, and it focuses on learning statistical tools needed to answer specific research questions. Thereafter it focuses on survey research, including survey administration. It then reviews the elements of research design, and in the end requires the students to conduct statistical analysis of the data obtained through the survey, and to present the research findings to the class. |
3 | |
| Qualitative Methodology ▼
Course Description
The course provides training in core methods expected to be required by students intending to undertake qualitative research, together with an understanding of broader qualitative approaches and methodologies within which they may be utilized. |
3 |
Major Elective Courses
| Study Area | Course | Credits |
|---|---|---|
| Major Electives | Machine Learning and Deep Learning ▼
Course Description
This course provides knowledge of machine learning and statistical pattern recognition. Topics include: supervised learning, unsupervised learning, learning theory, reinforcement learning, and adaptive control. This course will also discuss recent applications of machine learning, such as computer vision, speech recognition, and machine language translation have recently made great progress by using an emerging technology called deep learning. At its core, deep learning is inspired by a simplified model of how the human brain works by building effective hierarchical representations of complex data. This course will explore applications and theory relevant to problem-solving using deep learning. By the end of this course, students will gain intuition about how to apply various techniques judiciously and how to evaluate success. Students will also gain deeper insight into why certain techniques may work or fail for certain kinds of problems. |
3 |
| Computer Vision ▼
Course Description
This course covers technologies for computer vision. Topics include edge detection, image segmentation, stereopsis, motion and optical flow, image mosaics, 3D shape reconstruction, and object recognition. Students are required to implement several of the algorithms covered in the course and complete a final project. |
3 | |
| Natural Language Processing ▼
Course Description
This course is about a variety of ways to represent human languages as computational systems, covering the provenance of analysis and transformation of language by computational techniques. General linguistic preliminaries. Representations of text and speech that can aid prediction, extraction, and semantic reasoning over language. Automatic mining of knowledge from the web. The discipline of machine learning and its significance for NLP. Deep learning as a fundamental method for NLP. Recent technological developments in NLP, including automatic language translators such as Google Translate and personal assistants such as Siri. |
3 | |
| Computational Mathematics ▼
Course Description
This course provides knowledge and understanding of some of the most important scientific results in the area of computational mathematics. After completed studies, the student shall be able to understand and evaluate scientific work in the area of Computational Mathematics, and present advanced results in Computational Mathematics in written form and orally. |
3 | |
| Advanced Data Structures and Algorithms ▼
Course Description
This course covers the design, analysis, and implementation of data structures and algorithms to solve engineering problems using an object-oriented programming language. Topics include data structures (including arrays, stacks, queues, and lists), advanced data structures (including trees and graphs), the algorithms used to manipulate these structures, and their application to solving practical engineering problems. |
3 | |
| Data Analytics and Big Data ▼
Course Description
This course provides an introduction to big data and corresponding quantitative research methods. The objective of the course is to familiarize students with big data analysis as a tool for addressing substantive research questions. The course begins with a basic introduction to big data and discusses what the analysis of these data entails, as well as associated technical, conceptual and ethical challenges. Strength and limitations of big data research are discussed in depth using real-world examples. Students then engage in case study exercises in which small groups of students develop and present a big data concept for a specific real-world case. This includes practical exercises to familiarize students with the format of big data. It also provides a first hands-on experience in handling and analyzing large, complex data structures. The block course is designed as a primer for anyone interested in attaining a basic understanding of what big data analysis entails. |
3 | |
| Thesis Research* ▼
Course Description
This course is an area of study or research necessitating a high level of self-directed learning. This learning requires students to read, conduct research, complete written examinations, reports, projects, research papers, portfolios or similar assignments that are designed to measure competency in the stated educational objectives. The work will be related to an academic discipline done outside of the formal (directly supervised) classroom. This research may be experiential, directed reading or independent research supervised by a faculty advisor and approved by the chairperson of the department under which the course is listed. |
3 |
* Only applicable for thesis option candidates
General Electives
General Elective credits are any additional credits that can be earned in any category (Core, Major Required, or Major Elective)
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