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**Notes**

### 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 Ageâ€•and Why We Don't Have To: Notes on Harvard Professor David Sinclair's book on aging.