3. 강의목표
As machine learning and deep learning models are impacting on real world environments (e.g., ChatGPT), concerns on the trustworthiness of machine-learned models are rising. In this course, we explore whether popular machine-learned models are trustworthy and then study various learning methods to enchant the models to be trustworthy. To this end, we will learn basic knowledge on machine learning theory, uncertainty learning via conformal prediction, adversarial examples/learning, machine unlearning, differentially private learning, fairness in learning, and miscellaneous topics on trustworthy generative AI.
4. 강의선수/수강필수사항
- Optional class: Probability, Convex Optimization
- Required class: Machine Learning
5. 성적평가
https://sangdon.github.io/teaching/tml-2024-fall/
7. 참고문헌 및 자료
- Learning theory: Foundation of Machine Learning (https://cs.nyu.edu/~mohri/mlbook/)
- Conformal prediction: A Tutorial on Conformal Prediction (https://arxiv.org/pdf/0706.3188.pdf)
- Differential Privacy: The Algorithmic Foundations of Differential Privacy (https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf)
- Fairness: FAIRNESS AND MACHINE LEARNING (https://fairmlbook.org/pdf/fairmlbook.pdf)
8. 강의진도계획
https://sangdon.github.io/teaching/tml-2024-fall/
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