Large language models (LLMs) and other AI methods have grown radically in power and utility over the past few years. In general, this course will study AI at the human-machine frontier. In particular, this course will study several cutting edge ideas in machine learning through the lens of education applications.

Concepts to be discussed will include: AI alignment, Reinforcement learning from human feedback, Synthetic data training, Model editing, Model unlearning, LLMs for reasoning and planning, LLMs and mathematics, Student cognitive modeling, Bias/fairness of multimodal models, Attribution in LLMs, Long context modeling in LLMs, AI safeguards, AI and learning outcomes

• Location: Maxfield Hall 205
• Time: Friday 245pm
• Instructors: Richard Baraniuk & Debshila Basu Mallick 
  2028 Duncan Hall
  Office Hours: By appointment
• Prerequisites: Required: Linear algebra, introduction to probability and statistics, familiarity with a programming language such as Python, R, or MATLAB.  Desired: Knowledge of machine learning, signal processing, optimization, and deep learning
• Course Website:  Piazza course management site and Gradescope submission website
  (It is mandatory that you use these sites; all official announcements will be made there.)