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.
TJ Park Lib. Room 501
Sangdon Park
Kyungmin Kim (kkm959595@postech.ac.kr)
Date | Topic |
---|---|
[Week 1] 9/3 | Trustworthy ML Introduction |
[Week 1] 9/5 | Measure Theory: Introduction |
[Week 2] 9/10 | Learning Theory: PAC learning |
[Week 2] 9/12 | Learning Theory: Beyond PAC learning |
[Week 3] 9/17 | Korean Thanksgiving holiday |
[Week 3] 9/19 | Learning Theory: Beyond PAC learning |
[Week 4] 9/24 | Learning Theory: Online learning |
[Week 4] 9/26 | Learning Theory: Online learning |
[Week 5] 10/1 | Armed Forces Day |
[Week 5] 10/3 | National Foundation Day |
[Week 6] 10/8 | Controllable Uncertainty Learning: Conformal Prediction |
[Week 6] 10/10 | Controllable Uncertainty Learning: PAC Conformal Prediction |
[Week 7] 10/15 | Controllable Uncertainty Learning: Adaptive Conformal Prediction |
[Week 7] 10/17 | Controllable Uncertainty Learning: Selective Prediction |
[Week 8] 10/22 | Adversarial Learning: Adversarial Examples and Learning |
[Week 8] 10/24 | Adversarial Learning: Certified Adversarial Learning |
[Week 9] 10/29 | Differential Privacy: Basics |
[Week 9] 10/31 | Differential Privacy: Practice |
[Week 10] 11/5 | Machine Unlearning: Linear Models |
[Week 10] 11/7 | Machine Unlearning: Deep Models |
[Week 11] 11/12 | Fairness in Learning: Foundation |
[Week 11] 11/14 | [Fairness in Learning: NLP Application] |
[Week 12] 11/19 | Miscellaneous Topics in TML |
[Week 12] 11/21 | Final Exam |
[Week 13] 11/26 | Student Discussion 1 |
[Week 13] 11/28 | Student Discussion 2 |
[Week 14] 12/3 | Student Discussion 3 |
[Week 14] 12/5 | Student Discussion 4 |
[Week 15] 12/10 | Student Discussion 5 |
[Week 15] 12/12 | Student Discussion 6 |
[Week 16] 12/17 | Student Discussion 7 |
[Week 16] 12/19 | Student Discussion 8 |