Courses

Mathematical Foundations:

• Real Analysis, Functional Analysis

• Projection Theory

• High-dimensional Probability 

• Information Theory and Coding

• The Interplay between Convex Optimization and Geometry 

• Continuous Algorithms: Optimization and Sampling

Machine Learning, Reinforcement Learning, and Generative AI:

• Foundations of Deep Learning (U. Maryland)  (Videos)

• Foundations of Reinforcement Learning (Princeton) (Videos)

• TinyML and Efficient Deep Learning Computing (MIT) (Videos)

• Transformers and Large Language Models (Stanford) (Videos)

• Probabilistic Graphical Models (CMU) (Videos)

Modern Quantum Mechanics

• Quantum Physics I   Quantum Physics II   Quantum Physics III 

• Quantum Information Theory

• Particle Physics

Quantum Computing:

• Introduction to Quantum Computing (Columbia) 

• Frontiers of Quantum Complexity and Cryptography (Columbia)

• Quantum Computing (CalTech) (Videos)

• Quantum Computer Systems (Chicago) (Videos)

Useful Links

1. Francis Bach's Machine Learning Research Blog

2. Greg Yang's website on Neural Tangent Kernel

3. Jonathan Evans' math courses (Lie Algebra and Lie Groups; Topology and Groups)

4. IPAM Winter School: Quantum Error Suppression, Mitigation, and Correction

5. Agentic AI