Posted May 5, 2025

Twenty-five lectures

Stat 221, Statistical Computing and Visualization, taught the full cycle of a data project: models, computing, and interactive explanations. Slide decks lived on nesterko.com. The titles below are the ones listed on the blog.

1. 1Course Introduction
2. 2Introduction to Visualization, Modeling, and Computing
3. 3Intro VMC — Modeling and Computing
4. 4Guest lecture by Rachel Schutt: Introduction to Data Science
5. 5A More Rigorous Look at Visualization
6. 6Statistical Models and Likelihood
7. 7Likelihood Principle, MLE Foundations, Odyssey
8. 8Stochastic Optimization for Inference, Odyssey
9. 9Modeling with Missing Data and Latent Variables
10. 10Expectation-Maximization Algorithm
11. 11EM for HMMs, Properties of EM
12. 12EM variants, Data Augmentation
13. 13Likelihood + Prior = Posterior
14. 14Missing Data and MCMC
15. 15Hamiltonian Monte Carlo
16. 16Decision Theory and Statistical Inference
17. 17Parallel Statistical Computing
18. 18Parallel Tempering
19. 19Message Passing Interface for Parallel Tempering
20. 20Equi-Energy MCMC Sampler
21. 21Approximate Methods: Variational Inference
22. 22Variational EM, Monte Carlo EM
23. 23Hacker Level: Data Augmentation
24. 24Interactive Experiences and Us
25. 25The Final Lecture: Summing It Up