3. 강의목표
This is an introductory course on deep learning. The topics include, but not limited to, machine learning, deep neural network, regularization, optimization, graph learning and generative models. The students who successfully finish this course will gain knowledge on representation learning via deep learning.
4. 강의선수/수강필수사항
Basic knowledge of mathematics (such as linear algebra, calculus, probability) and computing skills (how to write a program).
5. 성적평가
| 중간고사 |
기말고사 |
출석 |
과제 |
프로젝트 |
발표/토론 |
실험/실습 |
퀴즈 |
기타 |
계 |
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| 비고 |
Assignments: 30%
Quiz: 30% (5 quizzes)
Final Project: 30% (making groups, project proposal, and report)
Class Participation: 10% (attendance and final project participation)
*The criteria above is subject to change.
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6. 강의교재
| 도서명 |
저자명 |
출판사 |
출판년도 |
ISBN |
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Understanding Deep Learning
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Simon J.D. Prince
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The MIT Press
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2023
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Deep Learning
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Ian Goodfellow
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An MIT Press Book
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2016
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7. 참고문헌 및 자료
https://udlbook.github.io/udlbook/
https://www.deeplearningbook.org/
8. 강의진도계획
Week 1. Course Overview / Introduction
Week 2. Linear Algebra
Week 3. Probability
Week 4. Numerical Computation
Week 5. Deep Feedforward Networks
Week 6. ML Basics
Week 7. Regularization
Week 8. Midterm week
Week 9. Optimization
Week 10. Convolution Neural Network
Week 11. Recurrent Neural Network
Week 12. Autoencoder / Transformer
Week 13. Graph Neural Network
Week 14. Generative Models
Week 15. Representation Learning
Week 16. Final week
*The course schedule is subject to change.
9. 수업운영
- Offline lectures.
- B- or below will be considered unsatisfactory for S/U.
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