notebook · javid's blog · est. 2026
~ teaching notes & posts ~

Blog & Notes

Notes from teaching Yale's SD&S 365/565 with Prof. John Lafferty and CPSC 481/581 with Prof. Andre Wibisono. If you have ideas to enhance the content, reach out.

§ 01

machine learning notes

Sequence Models & Recurrent Neural Networks
Reinforcement Learning
Sparsity and Graphs
Variational Inference
Approximation Inference
Gibbs Sampling
Dirichlet Processes
Bayesian Inference
Neural Tangent Kernels
Representer Theorem
Mercer's Theorem
Sparsity Meets Convexity
Expectation Maximization
Managing Large Brain Imaging Experiments
Mixture Models and EM
Posterior Inference
Empirical Risk Minimization
MLE and MAP Estimation
Stochastic Functions in Python
Kernel Methods
Optimal Transport
Optimal Transport & Convexity