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