Curriculum
Curriculum Design and Operation
1) Curriculum Structure
The program is designed to provide a balanced curriculum consisting of foundational courses such as AI fundamental theories, Formal Proof, and Interactive Proof Systems, along with advanced research-oriented courses focused on case studies and projects to enable practical application in mathematical research.
2) Master’s Program Requirements
- Master’s Program (Thesis Track)
- Total credits required for graduation: At least 36 credits
- Common Required: At least 3 credits
- Must complete at least one of the following courses
- CC.50000 Scientific Writing, CC.50010 Introduction to Computational Applications, CC.50013 Engineering Economics and Cost Analysis, CC.50030 Entrepreneurship and Business Strategy, CC.50032 Collaborative Systems Design
- CC.50011 Probability and Statistics is not accepted
- Must complete at least one of the following courses
- Major Required: None
- Electives: At least 24 credits
- Basic Designated Courses: At least 6 credits
- Complete at least one course (3 credits) offered by the Graduate School of AI Mathematics
- ※ May complete ‘Basic Designated’ courses from other departments designated by the Graduate School of AI Mathematics (*Refer to course list)
- Advanced Designated Courses: At least 18 credits
- Complete at least two courses (6 credits) offered by the Graduate School of AI Mathematics
- ※ May complete ‘Advanced Designated’ courses from other departments designated by the Graduate School of AI Mathematics (*Refer to course list)
- Basic Designated Courses: At least 6 credits
- Research: At least 9 credits
- Must complete AIM.93100 Seminar (Master’s) at least once
- Master’s Program (Coursework Track)
- Total credits required for graduation: At least 36 credits
- Common Required: At least 3 credits
- Must complete at least one of the following courses
- CC.50000 Scientific Writing, CC.50010 Introduction to Computational Applications, CC.50013 Engineering Economics and Cost Analysis, CC.50030 Entrepreneurship and Business Strategy, CC.50032 Collaborative Systems Design
- CC.50011 Probability and Statistics is not accepted
- Must complete at least one of the following courses
- Major Required: None
- Electives: At least 30 credits
- Basic Designated Courses: At least 6 credits
- Complete at least one course (3 credits) offered by the Graduate School of AI Mathematics
- ※ May complete ‘Basic Designated’ courses from other departments designated by the Graduate School of AI Mathematics (*Refer to course list)
- Advanced Designated Courses: At least 24 credits
- Complete at least two courses (6 credits) offered by the Graduate School of AI Mathematics
- ※ May complete ‘Advanced Designated’ courses from other departments designated by the Graduate School of AI Mathematics (*Refer to course list)
- Basic Designated Courses: At least 6 credits
- Research: At least 3 credits
- Must complete AIM.93100 Seminar (Master’s) at least once
- Presentation of a Project Research Report
- Other
- These requirements apply to students admitted from the Spring semester of the 2026 academic year.
3) Integrated Master’s–Doctoral and Doctoral Program Requirements
- Integrated Master’s–Doctoral Program
- Total credits required for graduation: At least 66 credits
- Common Required: At least 3 credits
- Must complete at least one of the following courses
- CC.50000 Scientific Writing, CC.50010 Introduction to Computational Applications, CC.50013 Engineering Economics and Cost Analysis, CC.50030 Entrepreneurship and Business Strategy, CC.50032 Collaborative Systems Design
- CC.50011 Probability and Statistics is not accepted
- Must complete at least one of the following courses
- Major Required: None
- Electives: At least 33 credits
- Basic Designated Courses: At least 6 credits
- Complete at least one course (3 credits) offered by the Graduate School of AI Mathematics
- ※ May complete ‘Basic Designated’ courses from other departments designated by the Graduate School of AI Mathematics (*Refer to course list)
- Advanced Designated Courses: At least 27 credits
- Complete at least two courses (6 credits) offered by the Graduate School of AI Mathematics
- ※ May complete ‘Advanced Designated’ courses from other departments designated by the Graduate School of AI Mathematics (*Refer to course list)
- Basic Designated Courses: At least 6 credits
- Research: At least 30 credits
- AIM.93100 Seminar (Master’s) or AIM.93200 Seminar (Doctoral), total of 2 times (2 credits)
- Doctoral Program
- Total credits required for graduation: At least 66 credits
- Common Required: At least 3 credits
- Must complete at least one of the following courses
- CC.50000 Scientific Writing, CC.50010 Introduction to Computational Applications, CC.50013 Engineering Economics and Cost Analysis, CC.50030 Entrepreneurship and Business Strategy, CC.50032 Collaborative Systems Design
- CC.50011 Probability and Statistics is not accepted
- Must complete at least one of the following courses
- Major Required: None
- Electives: At least 33 credits
- Basic Designated Courses: At least 6 credits
- Complete at least one course (3 credits) offered by the Graduate School of AI Mathematics
- ※ May complete ‘Basic Designated’ courses from other departments designated by the Graduate School of AI Mathematics (*Refer to course list)
- Advanced Designated Courses: At least 27 credits
- Complete at least two courses (6 credits) offered by the Graduate School of AI Mathematics
- ※ May complete ‘Advanced Designated’ courses from other departments designated by the Graduate School of AI Mathematics (*Refer to course list)
- Basic Designated Courses: At least 6 credits
- Research: At least 30 credits
- Must complete AIM.93200 Seminar (Doctoral) at least once
- Other
- These requirements apply to students admitted from the Spring semester of the 2026 academic year
- Credits earned in the Master’s program are accumulated and counted toward the Doctoral program
Courses: 10 courses (5 electives, 3 research, 2 seminars)
개설교과목 (교과목) - en
| Course Category | Course Code | Course Title | Instructor | Lecture:Lab:Credit | Semester Offered | Grading | Cross-listed (Undergraduate/Graduate) | |
|---|---|---|---|---|---|---|---|---|
| Elective (M.S./Ph.D.) | Foundational Required Course | AIM.50001 | Introduction to Mathematical Formalization | Department Faculty | 3:0:3 | Spring or Fall | A-F | Not Cross-listed |
| Elective (M.S./Ph.D.) | AIM.50002 | Interactive Theorem Proving | Department Faculty | 3:0:3 | Spring or Fall | A-F | Not Cross-listed | |
| Elective (M.S./Ph.D.) | Advanced Required Course | AIM.60001 | Automated Reasoning and Proof Search | Department Faculty | 3:0:3 | Spring or Fall | A-F | Not Cross-listed |
| Elective (M.S./Ph.D.) | AIM.60002 | AI for Theorem Proving | Department Faculty | 3:0:3 | Spring or Fall | A-F | Not Cross-listed | |
| Elective (M.S./Ph.D.) | AIM.60003 | AI-Driven Mathematical Discovery | Department Faculty | 3:0:3 | Spring or Fall | A-F | Not Cross-listed | |
| Seminar | AIM.93100 | Seminar (M.S.) | Department Faculty | 1:0:1 | Spring and Fall | S, U | Not Cross-listed | |
| Seminar | AIM.93200 | Seminar (Ph.D.) | Department Faculty | 1:0:1 | Spring and Fall | S, U | Not Cross-listed | |
| Individual Research | AIM.91200 | Individual Research (M.S.) | Department Faculty | 0:0:0 | Spring and Fall | S, U | Not Cross-listed | |
| Thesis Research | AIM.921000 | Thesis Research (M.S.) | Department Faculty | 0:0:0 | Spring and Fall | S, U | Not Cross-listed | |
| Thesis Research | AIM.922000 | Thesis Research (Ph.D.) | Department Faculty | 0:0:0 | Spring and Fall | S, U | Not Cross-listed | |
※ Effective from the Spring semester of the 2026 academic year
Recognized Courses from Other Departments: 31 courses
(17 from Mathematical Sciences, 6 from Kim Jaechul Graduate School of AI, 4 from School of Computing, 2 from Industrial and Systems Engineering, 2 from Graduate School of Data Science Program)
개설교과목 (타학과 인정 교과목) - en
| Course Category | Course Code | Course Title | Instructor | Lecture:Lab:Credit | Semester Offered | Grading | Cross-listed (Undergraduate/Graduate) | |
|---|---|---|---|---|---|---|---|---|
| Major Elective | Foundational Required Course | MAS.40073 | 수학과 인공지능 개론 | Department Faculty | 3:0:3 | Spring or Fall | A-F | Cross-listed |
| Elective (M.S./Ph.D.) | AI.50200 | 심층학습 | Department Faculty | 3:0:3 | Spring or Fall | A-F | Not Cross-listed | |
| Elective (M.S./Ph.D.) | Advanced Required Course | MAS.50011 | 대수학I | Department Faculty | 3:0:3 | Spring or Fall | A-F | Cross-listed |
| Elective (M.S./Ph.D.) | MAS.50020 | 미분기하학 | Department Faculty | 3:0:3 | Spring or Fall | A-F | Cross-listed | |
| Elective (M.S./Ph.D.) | MAS.50031 | 대수적 위상수학I | Department Faculty | 3:0:3 | Spring or Fall | A-F | Cross-listed | |
| Elective (M.S./Ph.D.) | MAS.50040 | 실변수함수론 | Department Faculty | 3:0:3 | Spring or Fall | A-F | Cross-listed | |
| Elective (M.S./Ph.D.) | MAS.50041 | 복소수함수론 | Department Faculty | 3:0:3 | Spring or Fall | A-F | Cross-listed | |
| Elective (M.S./Ph.D.) | MAS.50050 | 확률론 | Department Faculty | 3:0:3 | Spring or Fall | A-F | Cross-listed | |
| Elective (M.S./Ph.D.) | MAS.50055 | 고급통계학 | Department Faculty | 3:0:3 | Spring or Fall | A-F | Cross-listed | |
| Elective (M.S./Ph.D.) | MAS.50057 | 기계학습이론 및 응용 | Department Faculty | 3:0:3 | Spring or Fall | A-F | Cross-listed | |
| Elective (M.S./Ph.D.) | MAS.50065 | 수치해석학 | Department Faculty | 3:0:3 | Spring or Fall | A-F | Cross-listed | |
| Elective (M.S./Ph.D.) | MAS.50075 | 조합수학 | Department Faculty | 3:0:3 | Spring or Fall | A-F | Cross-listed | |
| Elective (M.S./Ph.D.) | MAS.60030 | 기하학적 위상수학 | Department Faculty | 3:0:3 | Spring or Fall | A-F | Not Cross-listed | |
| Elective (M.S./Ph.D.) | MAS.60050 | 확률미분방정식론 | Department Faculty | 3:0:3 | Spring or Fall | A-F | Not Cross-listed | |
| Elective (M.S./Ph.D.) | MAS.60051 | 확률과정론 | Department Faculty | 3:0:3 | Spring or Fall | A-F | Not Cross-listed | |
| Elective (M.S./Ph.D.) | MAS.60057 | 신경회로망의 수리적 모델 | Department Faculty | 3:0:3 | Spring or Fall | A-F | Not Cross-listed | |
| Elective (M.S./Ph.D.) | MAS.70010 | 표현론 | Department Faculty | 3:0:3 | Spring or Fall | A-F | Not Cross-listed | |
| Elective (M.S./Ph.D.) | MAS.70012 | 대수적 정수론 | Department Faculty | 3:0:3 | Spring or Fall | A-F | Not Cross-listed | |
| Elective (M.S./Ph.D.) | CS.40701 | 그래프 기계학습 및 마이닝 | Department Faculty | 3:0:3 | Spring or Fall | A-F | Cross-listed | |
| Elective (M.S./Ph.D.) | CS.50700 | 인공지능 및 기계학습 | Department Faculty | 3:0:3 | Spring or Fall | A-F | Cross-listed | |
| Elective (M.S./Ph.D.) | CS.60701 | 고급 기계학습 | Department Faculty | 3:0:3 | Spring or Fall | A-F | Not Cross-listed | |
| Elective (M.S./Ph.D.) | CS.60702 | 강화학습 | Department Faculty | 3:0:3 | Spring or Fall | A-F | Not Cross-listed | |
| Elective (M.S./Ph.D.) | IE.50040 | 동적계획법 및 강화학습 | Department Faculty | 3:1:3 | Spring or Fall | A-F | Cross-listed | |
| Elective (M.S./Ph.D.) | IE.50039 | 컨벡스 최적화 | Department Faculty | 3:1:3 | Spring or Fall | A-F | Cross-listed | |
| Elective (M.S./Ph.D.) | DS.50033 | 통계적 생성모델 | Department Faculty | 3:0:3 | Spring or Fall | A-F | Cross-listed | |
| Elective (M.S./Ph.D.) | DS.50035 | 추천시스템 및 그래프기계학습 | Department Faculty | 3:0:3 | Spring or Fall | A-F | Cross-listed | |
| Elective (M.S./Ph.D.) | AI.60500 | 자연어 처리를 위한 심층학습 기법 | Department Faculty | 3:0:3 | Spring or Fall | A-F | Not Cross-listed | |
| Elective (M.S./Ph.D.) | AI.61100 | 생성모델과 비지도 학습 | Department Faculty | 3:0:3 | Spring or Fall | A-F | Not Cross-listed | |
| Elective (M.S./Ph.D.) | AI.61800 | 대형언어모델 | Department Faculty | 3:0:3 | Spring or Fall | A-F | Not Cross-listed | |
| Elective (M.S./Ph.D.) | AI.70500 | 고급 심층 강화학습 | Department Faculty | 3:0:3 | Spring or Fall | A-F | Not Cross-listed | |
| Elective (M.S./Ph.D.) | AI.70700 | 고급 심층 강화학습 | Department Faculty | 3:0:3 | Spring or Fall | A-F | Not Cross-listed | |
※ Effective from the Spring semester of the 2026 academic year