This is a tentative schedule.

(Wed, 1/14) Lecture 1: Course Overview [slides]

(Wed, 1/21) Lecture 2: Review of ML Models [slides]

(Mon, 1/26) University Closed

(Wed, 1/28) Lecture 3: Adversarial Robustness [slides]

(Mon, 2/2) Lecture 4: Jailbreaking LLMs [slides]

(Wed, 2/4) Lecture 5: Robust Training [slides]

(Wed, 2/4) HW 1 Released [written] [programming]

(Mon, 2/9) Lecture 6: Formal Methods for Robustness Verification [slides]

(Wed, 2/11) Lecture 7: Label Shift [slides]

(Thurs, 2/12) HW 1 Due

(Mon, 2/16) Lecture 8: Covariate Shift [slides]

(Wed, 2/18) Lecture 9: Conformal Prediction [slides]

(Wed, 2/18) HW 2 Released [written] [programming]

(Mon, 2/23) Lecture 10: Calibrated Prediction [slides]

(Wed, 2/25) Lecture 11: Uncertainty Quantification for LLMs [slides]

(Thurs, 2/26) HW 2 Due

(Mon, 3/2) Lecture 12: Review

(Wed, 3/4) Lecture 13: Midterm 1

(Mon, 3/16) Lecture 14: Explainability [slides]

(Wed, 3/18) Lecture 15: Explainability: SHAP [slides]

(Mon, 3/23) Lecture 16: Counterfactual and Concept-Based Explanations [slides]

(Wed, 3/25) Lecture 17: Data Attribution Methods [slides]

(Wed, 3/25) HW 3 Released [written] [programming]

(Mon, 3/30) Lecture 18: Background on Reinforcement Learning [slides]

(Wed, 4/1) Lecture 19: Reinforcement Learning from Human Feedback [slides]

(Thurs, 4/2) HW 3 Due

(Mon, 4/6) Lecture 20: Direct Policy Optimization [slides]

(Wed, 4/8) Lecture 21: Explainability and Alignment Case Study: ECG Classification [slides]

(Mon, 4/13) Lecture 22: Review

(Wed, 4/15) Lecture 23: Midterm 2

(Mon, 4/20) Lecture 24: Neurosymbolic Learning

(Wed, 4/22) Lecture 25: Neurosymbolic Learning

(Mon, 4/27) Lecture 26: Project Presentations

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