Department of Artificial Intelligence
The Department of Artificial Intelligence is dedicated to fortifying academic knowledge and technical skills in artificial intelligence, which is rapidly developing and gaining attention in modern society. Artificial intelligence is a core major that fosters human resources needed for the future era of AI, and the department provides students with fundamental theories and field experiences to cultivate the ability to cope with various challenges in modern society. The department provides students with systematic and in-depth education based on a deep understanding of basic disciplines such as computer science, mathematics, and statistics. In our department, students learn core concepts and skills in areas such as machine learning, deep learning, natural language processing, and robotics. In addition to theoretical knowledge, students develop problem-solving skills through real-world application projects and acquire technical competencies required by the industry. In addition, students can enhance their practical skills through various experiences such as cooperative projects with industrial companies, artificial intelligence competitions, and participation in international academic conferences.
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| Courses | Credits | |
|---|---|---|
| *General Studies (required) Total: 22 credits |
College Writing | 2 |
| Critical Thinking and Discussion with Classics | 2 | |
| College English(W) | 2 | |
| College English(S) | 2 | |
| Calculus Ⅰ | 3 | |
| Calculus II | 3 | |
| AI and SW Basics for Engineering | 3 | |
| AI and SW Applications for Engineering | 3 | |
| UOS Future Design | 1 | |
| UOS Career Design | 1 | |
| General Studies (elective) Total: 16 credits |
Engineering Grounding | 8 |
| *Elementary Major Course Study
*Physics Experiment 1,2 |
8 | |
| Major Courses |
Required | 38 |
| Elective | 48 | |
| Other |
|
10 |
| Total |
|
130 |
| Course Number |
Course Title | School Year | Semester | Credit | Lecture (hours) |
Practice (hours) |
|---|---|---|---|---|---|---|
| 20003 | STUDY-PLANNING COUNSELINGⅠ | 1 | Spring | S/U | 1 | 0 |
| 20004 | STUDY-PLANNING COUNSELINGⅡ | Fall | S/U | 1 | 0 | |
| 20040 | Computational Thinking and SW Coding | Spring | 3 | 3 | 0 | |
| 20055 | Introduction to Statistics for AI | Fall | 3 | 2 | 2 | |
| 20007 | Programming Basics and Practice | Fall | 3 | 2 | 2 | |
| 20005 | Problem Solving and Algorithm | 2 | Spring | 3 | 3 | 0 |
| 20006 | Discrete Mathematics with Applications | Spring | 3 | 3 | 0 | |
| 20009 | Computer Systems | Spring | 3 | 0 | 0 | |
| 20010 | Algorithm | Fall | 3 | 3 | 0 | |
| 20011 | Knowledge Representation and Reasoning | Fall | 3 | 3 | 0 | |
| 20012 | Machine Learning | Fall | 3 | 3 | 0 | |
| 20013 | Linear Algebra with Applications | Fall | 3 | 3 | 0 | |
| 20045 | Statistical Methods for Data Science | Fall | 3 | 3 | 0 | |
| 20046 | Statistical Inference | Fall | 3 | 0 | 0 | |
| 20049 | Programming Languages | Spring | 3 | 3 | 0 | |
| 20051 | On Device Artificial Intelligence | Fall | 3 | 3 | 0 | |
| 20056 | Probability and Random Processes for AI | Spring | 3 | 3 | 0 | |
| 20014 | Understanding and Leveraging Databases | 3 | Fall | 3 | 2 | 2 |
| 20015 | Deep Learning | Spring | 3 | 3 | 0 | |
| 20016 | Data Mining | Spring | 3 | 3 | 0 | |
| 20017 | Advanced Data Analysis | Spring | 3 | 3 | 0 | |
| 20018 | Intelligent Robots | Fall | 3 | 3 | 0 | |
| 20019 | Intelligent Human Computer Interaction | Fall | 3 | 3 | 0 | |
| 20020 | AI-based Software Engineering | Fall | 3 | 3 | 0 | |
| 20021 | Pattern Recognition | Fall | 3 | 3 | 0 | |
| 20052 | Parallel Computing | Spring | 3 | 3 | 0 | |
| 20053 | Artificial Intelligence Algorithms | Spring | 3 | 3 | 0 | |
| 20060 | Multimodal AI | Fall | 3 | 3 | 0 | |
| 20023 | Artificial Intelligence Systems | 4 | Fall | 3 | 2 | 2 |
| 20024 | Capstone Design 1 | Spring | 3 | 2 | 2 | |
| 20025 | The Ethics of Artificial Intelligence | Spring&Fall | 3 | 3 | 0 | |
| 20026 | Natural Language Processing | Fall | 3 | 3 | 0 | |
| 20027 | Artificial Intelligence Living Lab | Fall | 3 | 2 | 2 | |
| 20028 | Capstone Design2 | Fall | 3 | 2 | 2 | |
| 20041 | Reinforcement Learning | Fall | 3 | 3 | 0 | |
| 20047 | Introduction to Computer Vision | Spring | 3 | 0 | 0 | |
| 20054 | Reliable and Trustworthy AI | Spring&Fall | 3 | 3 | 0 | |
| 20057 | Healthcare Big Data and AI | Spring | 3 | 3 | 0 | |
| 20043 | Information Theory & Data Science | Spring | 3 | 3 | 0 | |
| 20042 | Speech Recognition and Synthesis | Spring | 3 | 0 | 0 |
