Hi I'm Chandan, a Senior Researcher at Microsoft Research working on interpretable machine learning. I've been compulsively taking / improving my notes since my PhD at UC Berkeley and share them on this website. Hope they're helpful :)
Slides •
Research overviews •
Cheat sheets •
Notes
Blog posts •
Personal info
@csinva
Slides
The pres folder contains source for presentations, including ML slides from teaching machine learning at berkeley
The source is in markdown (built with reveal-md) and is easily editable / exportable
- ML slides (berkeley cs 189)
- AI slides (berkeley cs 188)
- Interpretability workshop
- Disentangled interpretations
Research and class notes
The research_ovws folder contains overviews and summaries of recent papers in different research areas
- Interpretability
- Causal inference
- Transfer learning
- Uncertainty
- DL theory
- Complexity
- Scattering transform
- DL in neuroscience
The _notes folder contains markdown notes and cheat-sheets for many different courses and areas between computer science, statistics, and neuroscience
- Interpretability cheat sheet
- Computational neuroscience
- Causal inference notes
- Classification
- Linear algebra
- Info theory
- Computer vision
- Way more notes here
Posts
Posts on various aspects of machine learning / statistics / neuroscience advancements (some selected posts below)
- paper writing tips(2023)
- forecasting paper titles (2022)
- imodels (2022, bairblog)
Reference
- For updates, star the repo or follow @csinva
- Feel free to use openly!
- Built with jekyll | github pages | timeline theme | particles.js | jupyterbook
