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

Blog & Notes

These notes grew out of my work as a teaching fellow for machine-learning courses at Yale, with John Lafferty and Andre Wibisono. They cover the models, probability, and optimization ideas that also appear in my research. Corrections and questions are welcome.

§ 01

machine learning notes

Sequence Models & Recurrent Neural Networks
Reinforcement Learning
Sparsity and Graphs
Variational Inference
Approximate 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