대학원 교과목 이수체계
교과목
| 코드번호 | 교 과 목 명 | 영 문 명 |
|---|---|---|
| G62615 | 어낼리틱스 이론 및 응용 | Analytics Theory and Its Applications |
| G62631 | 기술경영분석방법론 | Analysis Method in Management of Technology |
| G54574 | 통신경영정책특론 | Special Topics in Telecommunications Management Policy |
| G62633 | 기술사업화론 | Technology Commercialization |
| G62626 | 데이터마이닝응용 | Data Mining Applications |
| G62621 | 머신비젼 | Machine Vision |
| G62625 | 물류시스템분석 및 설계 | Analysis and Design of Logistics Systems |
| G62619 | 물류정보시스템 | Logistics Information Systems |
| G62616 | 발견적기법 | Heuristic and Meta-heuristic Methods |
| G54567 | 제품공학 | Product Engineering |
| G62639 | 에이전트기반 모델링 | Agent Based Modeling |
| G54573 | 기술혁신전략 | Technology Innovation Strategies |
| G62640 | 생산시스템 특론 | Special Topics in Production Systems |
| G54576 | 통계적 학습 | Statistical Learning |
| G62634 | 산업경영공학세미나 Ⅰ | Seminar in Industrial and Management Engineering Ⅰ |
| G62635 | 산업경영공학세미나 Ⅱ | Seminar in Industrial and Management Engineering Ⅱ |
| G62624 | 데이터어낼리틱스 세미나 1 | Seminar in Data Analytics 1 |
| G62609 | 생산정보시스템 | Production Information Systems |
| G62638 | 시스템공학및응용 | Systems Engineering and Its Applications |
| G62506 | 소프트웨어품질경영 | Software Quality Management |
| G62605 | 스케줄링 | Scheduling |
| G62637 | 데이터 공학 | Data Engineering |
| G62641 | 프로세스 마이닝 | Process mining |
| G62636 | 인공 신경망 | Artificial Neural Networks |
| G62601 | 이산최적화 | Discrete Optimization |
| G54575 | 시스템 다이내믹스 | System Dynamics |
| G54572 | 지능형 컴퓨팅 | Computational Intelligence |
| G62603 | 자동화시스템고등논제 | Special Topics in Automation |
| G62607 | 비정형 데이터 분석 | Unstructured Data Analysis |
| G62623 | 정보공학고등논제 | Special Topics in Information Engineering |
| G62614 | 지능형생산시스템 | Intelligent Manufacturing Systems |
| G62622 | 통신시스템고등논제 | Special Topics in Telecommunications |
| G62613 | 품질공학특론 | Special Topics in Quality Engineering |
| G62620 | 품질정보시스템 | Quality Information Systems |
| G62602 | 빅데이터 시스템 | Big Data Systems |
| G62611 | SCM특론 | Special Topics in Supply Chain Management |
| 수리적 확률 및 통계 | Mathematical Probability & Statistics | |
| 응용 확률적 과정 | Applied Stochastic Processes | |
|
|
운영분석 |
Operational analytics |
종합시험 과목
1.종합시험은 아래 9과목으로 구성되며, 석사과정은 3개를 박사과정은 4개를 선택한다 – The qualify exam consists of 6 subjects as below. Among them, Master and Ph.D. candidates choose 3 and 4 subjects, respectively:
(1) Analytics theory and its applications
(2) Data mining applications
(3) Special topics in information engineering
(4) Computational intelligence
(5) Data engineering
(6) Artificial neural networks
(7) Agent based modeling
(8) Operational analytics
(9) Technological innovation strategy
2. 시험은 과목당 2시간-오픈북, 통합 시험을 원칙으로 하되, 담당 교수에 따라 변경될 수 있다 – The exam requires 2 hours with open-book for each subject, but not be separated by subjects. The professor in charge of a subject may change the format of the subject exam.
3. 응시 학생은 담당 교수에게 세부 내용을 사전에 확인해야 한다 – A candidate is supposed to consult with the professor for the scope and details of a subject, in advance.
4. 각 과목시험의 70점 이상을 통과로 판정하며, 모든 과목을 통과하면 종합시험 합격으로 판정한다 – Each subject is passed if its score is not less than 70/100, and the exam is passed, only if all subjects are passed.
