Course Number(s):
Physics 265
Instructor(s):
School(s):
Semester:
- Spring 2023
Course Level:
- Graduate, Undergraduate
Harvard Course Description_Nov28.pdf
Class Days/Times:
Mon 3:00pm to 4:15pm
Wed 3:00pm to 4:15pm
Location:
TBD
Prerequisite(s):
Mastering of linear algebra, multi-variate calculus, probabilities, complex analysis; basic coding skills (Matlab or Python).
Recommended:
familiarity with the fundamentals of statistical mechanics, (including random walks, Markov processes, Gibbs distribution, phase transitions, Ising model).
Course Description:
The theory of random magnetic systems, spin-glasses, has transformed our understanding of the impact of disorder and complexity in many areas such as physics, biology, computer science, statistics, neuroscience, and AI. The purpose of the course is to survey advanced spin glass theoretical approaches, including Replica Theory, Dynamic Mean Field Theory, the cavity method, and belief propagation. Applications include the physics of spin glasses, combinatorial optimization, random matrices, chaos in random recurrent networks, associative memory, and learning in deep neural networks.
Course level: primarily for graduate students. Advanced undergraduate students accepted by permission of course instructor.