Course Notes
Notes and materials from classes I have taken at UC Berkeley.
Machine Learning & AI
CS 189 — Introduction to Machine Learning
CS 182 — Designing, Visualizing and Understanding Deep Neural Networks
CS 185 — Deep Reinforcement Learning
Taught by Sergey Levine.
EECS 183 — Natural Language Processing
Taught by Gopala Anumanchipalli and Alane Suhr.
CS 288 — Natural Language Processing (Graduate)
Taught by Sewon Min and Alane Suhr.
EE 194 — Scalable AI
Taught by Jiantao Jiao and Anant Sahai.
CDSS 94 — Post-Training AI Systems
Learned a lot with Karina Nguyen and Kevin Miao.
Mathematics & Theory
Math 54 — Linear Algebra and Differential Equations
Math 110 — Linear Algebra
Math 128A — Numerical Analysis
EECS 126 — Probability and Random Processes
Taught by Kannan Ramchandran.
EECS 127 — Optimization Models in Engineering
Taught by Gireeja Ranade.
Entrepreneurship
UGBA 195T — Startup Relationships
My time with Jules is remarkable. I am really grateful to be one of his students
Self-Study
Deep RL Bootcamp · lectures
OpenAI Spinning Up · RL notes
The Ultra-Scale Playbook · distributed training
veRL · RL post-training systems
OpenRLHF · RLHF framework
RLHF Book · post-training notes