Artificial Intelligence and Machine Learning Methods With Applications to Physics

The course will introduce key ideas of AI and machine learning from a statistical physics perspective. Essentials of statistical distributions, kernel methods, neural networks, large language models, diffusion models, and many other tools will be presented from this physics based approach. Students will apply these techniques to problems in physics. Apart from the very first assignments, nearly all homework problems will assume the use of Python. If you do not know Python, there will be an additional very brief introduction to Python that the instructor will give in addition to the course lectures. Prerequisites: Graduate Student standing or L24 132 (Calculus III) and L24 429 (Linear Algerbra)
Course Attributes:

Artificial Intelligence and Machine Learning Methods With Applications to Physics

PHYSICS 5080

Section 01

Instructors

Zohar Nussinov

Professor of Physics