ML Notes

• Robotic Learning Notes: Notes on robotic learning papers. Includes AWAC, RIG, CCRIG, VAL, C-Learning, VaPRL, Bridge Data.
• Unsupervised Learning notes: Notes on unsupervised learning papers. Includes VAE, VQVAE, VIB.

Course Notes

• CS285 (in progress): Deep Reinforcement Learning
• CS182: Designing, Visualizing and Understanding Deep Neural Networks
• EECS126: Probability and Random Processes

Math Notes

• Block Matrix Inverse and Woodbury Formula: In this note, I go into the proof of the block-wise matrix inverse and Woodbury Formula.
• 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.
• 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.
• 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.

Book Notes

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