Courses

Mathematical Foundations:

• Real Analysis, Functional Analysis

• Projection Theory

• High-dimensional Probability 

• The Interplay between Convex Optimization and Geometry 

• Continuous Algorithms: Optimization and Sampling

• Topics in Mathematics with Applications in Finance

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) 

• Deep Reinforcement Learning (Stanford) (Videos)

Quantum Computation and Quantum Information

• Introduction to Quantum Computing (Columbia) 

• Frontiers of Quantum Complexity and Cryptography (Columbia)

• Quantum Computing (CalTech) (Videos)

• Quantum Computer Systems (Chicago) (Videos)

• Quantum Information Theory (Waterloo)

Useful Links

1. Francis Bach's Machine Learning Research Blog

2. Greg Yang's website on Neural Tangent Kernel

3. Nathan Wiebe's Lectures on Block Encoding and Quantum Signal Processing

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

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

6. Advanced Deep Learning for Physics