3. 강의목표
(Course Objective)
The objective of this course is to provide some basic but very important notions of Artificial Intelligence with several real world applications in Chemical Engineering. The course consists of three parts. In the first part, as preparation for the studies of Artificial Intelligence, essential basic numerical techniques of Linear Algebra, Optimization, and Ordinary Differential Equations are covered. In the second part, basic AI algorithms such as Neural Network, Machine Learning, and Deep Learning are studied with the help of the already covered notions in the first part. In the final part, real world application problems are introduced for the students to have vivid feeling of AI applications in the chemical engineering field. .As numerical tools, Matlab and/or Python are used and the students will be encouraged to write their own codes to solve some important problems.
4. 강의선수/수강필수사항
(Prerequisite)
Undergrad math (1st year)
5. 성적평가
(Evaluation)
Homework: 100
Coding (3 sets): 100
Final: 100
7. 참고문헌 및 자료
Will be introduced
8. 강의진도계획
(Lecture Plan)
Part 1: Preparation for the AI studies
Week 1: Introduction to the numerical tools: Matlab and Python
Week 2: Linear algebra (1): Solution of linear systems, Gauss elimination
Week 3: Linear algebra (2): Determinant, Eigenvalue problems, Hermitian matrix
Week 4: Optimization (1); Steepest descent method
Week 5: Optimization (2): Conjugate gradient method
Week 6: Ordinary Differential equations: 1st order ODE, 2nd order ODE
Part 2: Basic algorithms in AI
Week 7: Neural network
Week 8: Machine learning: Back propagation algorithm
Week 9: Machine learning: Effects of activation functions
Week 10: Machine learning: Over-estimation and Regularization
Week 11: Deep learning: Convolutional neural network (CNN)
Part 3: Applications of AI in Chemical Engineering
Week 12: Applications in Petroleum industry
Week 13: Applications in material processing and material design
Week 14: Applications in Biotechnology
Week 15: Summary of the course
Week 16: Final exam
10. 학습법 소개 및 기타사항
TAs:
S.J. Lee (이선재) : imleesj@postech.ac.kr
H.J. Kim (송민재) : hyunjun1104@postech.ac.kr
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