2. 강의교수 정보
|
이름 |
신동일 |
학과(전공) |
기계공학과 |
| 이메일 주소 |
dongilshin@postech.ac.kr
|
Homepage |
|
| 연구실 |
데이터 기반 고체 역학 연구실 |
전화 |
054-279-2181 |
| Office Hours |
화(17:00~18:00)
|
3. 강의목표
This course introduces core machine learning algorithms with a focus on applications in solid mechanics.
Students will learn to implement and apply models using Python to analyze engineering data such as stress-strain behavior, material classification, and surrogate modeling of FEM outputs.
While key mathematical concepts like linear algebra and optimization are covered, the emphasis is on practical understanding and hands-on coding.
Topics include: Python programming, linear algebra, optimization, regression, classification, clustering, statistics, PCA/SVD, neural networks, Bayesian optimization, etc.
4. 강의선수/수강필수사항
* Basic experience with Python programming
* Familiarity with linear algebra and introductory calculus
* Prior coursework in mechanics of materials or solid mechanics is recommended
5. 성적평가
Attendance (10%) / Homework (20%) / Midterm (20%) / Final Exam (30%) / Project (20%)
6. 강의교재
| 도서명 |
저자명 |
출판사 |
출판년도 |
ISBN |
|
TBD
|
|
|
0000
|
|
8. 강의진도계획
Week 1: Introduction & Python Review
Week 2: Linear Algebra Refresher
Week 3: Optimization I
Week 4: Optimization II
Week 5: Regression Models
Week 6: Classification Techniques
Week 7: Model Evaluation
Week 8: Midterm Exam
Week 9: Engineering Statistics
Week 10: Dimension Reduction (PCA, SVD)
Week 11: Neural Networks I with PyTorch
Week 12: Neural Networks II – Surrogate Modeling
Week 13: Bayesian Concepts
Week 14: Bayesian Optimization
Week 15: Final Project Presentations
Week 16: Final Exam
11. 장애학생에 대한 학습지원 사항
- 수강 관련: 문자 통역(청각), 교과목 보조(발달), 노트필기(전 유형) 등
- 시험 관련: 시험시간 연장(필요시 전 유형), 시험지 확대 복사(시각) 등
- 기타 추가 요청사항 발생 시 장애학생지원센터(279-2434)로 요청