On this page I plan to collect my papers, presentations, notes, worked exercises, and anything else that seems relevant.
- BDA Ch2-3 Notes
- My notes for Gelman et al.’s Bayesian Data Analysis.
- MLPR Ch13-14 Notes
- My personal notes for Kevin Murphy’s Machine Learning: A Probabilistic Perspective. I hope to eventually cover the whole book, but I imagine that might take me some time!
- Few-Shot Learning
- A slideshow I put together that summarizes two interesting papers (Vinyals et al. Matching Networks for One-shot Learning and Snell et al. Prototypical Networks for Few-shot Learning) about few-shot learning.
- ESL Ch2 Solutions
- My personal solutions to exercises in Elements of Statistical Learning by Hastie, Tibshirani, and Friedman. So far I only have them for Chapter 2, but I hope to add more soon.
- Gaussian Measures on Hilbert Spaces
- A short report giving an introduction to Gaussian measures and Hilbert spaces, mostly referring to Da Prato’s Stochastic Equations in Infinite Dimensions.
- A Taste of Information Geometry
- A short report giving an introduction to a really interesting field, Information Geometry, which uses tools from differential geometry to study statistical models.