Course Notes

• [8/12/21] CS285 (incomplete): Deep Reinforcement Learning
• [5/21/21] CS182: Designing, Visualizing and Understanding Deep Neural Networks
• [5/13/21] EECS126: Probability and Random Processes

Math Notes

• [6/3/20] Equivalence of Jointly Gaussian Random Vector Definitions: In this note, I prove the equivalence of the multiple different definitions of jointly Gaussian random vectors and go into some misconceptions regarding them.
• [5/30/20] Determinants: In this note, I develop the definition of determinants from multilinearity and use this definition to derive its permutation definition, cofactor expansion, and various other determinant properties.
• [5/29/20] Marginal and Conditional Distributions of Multivariate Gaussians: In this note, I use the block-wise matrix inverse and Woodbury Formula to derive the marginal and conditional distributions of multivariate gaussians.
• [5/28/20] Block Matrix Inverse and Woodbury Formula: In this note, I go into the proof of the block-wise matrix inverse and Woodbury Formula.

ML Notes

• [6/22/22] Unsupervised Learning: Notes on unsupervised learning papers. Includes VAE, VQVAE, VIB.
• [8/31/21] Offline Goal-Conditioned Reinforcement Learning for Robotics: Notes on robotic learning papers relevant to the research I was doing as an undergrad. Includes AWAC, RIG, CCRIG, VAL, C-Learning, VaPRL, Bridge Data.

Book Notes

• [7/21/21] Lifespan: Why We Age―and Why We Don't Have To: Notes on Harvard Professor David Sinclair's book on aging.