This course explores the intersection of physics and computation in high dimensions. The learning outcomes include understanding the problems solvable using a computer, exploring phase transitions, message passing, sparse PCA, compressed sensing, deep learning, and neural networks. The teaching method involves lectures, discussions, and proofs. The course is intended for individuals interested in the applications of physics in computational problems.
Overview
Syllabus
Introduction
About the speaker
Problems we can solve using a computer
Phase Transitions
Summary
Rename
Accuracy
Message Passing
Phase Transition
Sparse PCA
No polynomial algorithm
Is this a special case
Compressed sensing
Interaction graph
Proofs
Deep learning
Neural networks
Structure of data
Conclusion
Questions
References
Q A
Thank you
Taught by
Joint Mathematics Meetings
