This is a tentative schedule.

(Mon, 1/22) Lecture 1: Introduction and Review [slides]

(Wed, 1/24) Lecture 2: Introduction and Review [slides]

(Mon, 1/29) Lecture 3: Distributional Robustness [slides]

(Wed, 1/31) Lecture 4: Distributional Robustness [slides]

(Mon, 2/5) Lecture 5: Adversarial Robustness [slides]

(Wed, 2/7) Lecture 6: Robust Training [slides]

(Mon, 2/12) Lecture 7: Guest Lecture by Alex Robey on Robustness for LLMs [slides] [HW 1]

(Wed, 2/14) Lecture 8: Formal Methods for Robustness Verification [slides]

(Mon, 2/19) Lecture 9: Verifying Robustness [slides]

(Wed, 2/21) Lecture 10: No class

(Mon, 2/26) Lecture 11: Calibrated Prediction [slides]

(Wed, 2/28) Lecture 12: Conformal Prediction [slides] [HW 2]

(Mon, 3/11) Lecture 13: Conformal Prediction [slides]

(Wed, 3/13) Lecture 14: Aleatoric vs. Epistemic Uncertainty [slides]

(Mon, 3/18) Lecture 15: Fairness Definitions [slides]

(Wed, 3/20) Lecture 16: Verifying Fairness [slides]

(Mon, 3/25) Lecture 17: Topics in Fairness [slides]

(Wed, 3/27) Lecture 18: Explainability [slides]

(Mon, 4/1) Lecture 19: LIME and SHAP [slides]

(Wed, 4/3) Lecture 20: Provably Robust Feature Attribution [part 1 slides] [part 2 slides]

(Mon, 4/8) Lecture 21: No class

(Wed, 4/10) Lecture 22: Counterfactual Explanations; Concept-Based Explanations [slides]

(Mon, 4/15) Lecture 23: Data Attribution Methods [slides]

(Wed, 4/17) Lecture 24: Neurosymbolic Learning (guest lecture by Ziyang Li) [slides]

(Mon, 4/22) Lecture 25: Project Presentations

(Wed, 4/24) Lecture 26: Project Presentations

(Mon, 4/29) Lecture 27: Project Presentations

(Wed, 5/1) Lecture 28: Project Presentations