Introduction to csinva.github.io
csinva.github.io is an extensive and meticulously curated online resource developed by Chandan, a Senior Researcher at Microsoft Research with a focus on interpretable machine learning. The website is designed to share Chandan's comprehensive notes and insights, accumulated since his PhD days at UC Berkeley, in order to assist and enlighten those interested in various fields of machine learning, computer science, and related disciplines.
Slides
The Slides section of the website provides access to a variety of educational materials and presentations. These materials are primarily created for teaching purposes and cover topics from machine learning courses taught at UC Berkeley. The presentations utilize markdown and are easily editable, making them a flexible resource for educators and learners alike. Here's a glimpse into what's available:
- ML slides for Berkeley CS 189: A rich collection for understanding machine learning concepts.
- AI slides for Berkeley CS 188: Explore artificial intelligence topics in depth.
- Interpretability workshop slides: These slides delve into understanding model interpretability.
- Disentangled Interpretations: Offers insights into providing clearer model interpretations.
Research and Class Notes
The Research and Class Notes section is a treasure trove of information, featuring overviews of recent papers across diverse research areas. These summaries help keep enthusiasts and professionals updated about current trends and breakthroughs in fields such as:
- Interpretability: Understanding how models make decisions.
- Causal Inference: Exploring methods to determine causality in data.
- Transfer Learning: Techniques for transferring knowledge across tasks.
- Uncertainty: Addressing the uncertainties in predictive models.
- Deep Learning Theory and its applications in Neuroscience: Detailed exploration of how deep learning theories are being applied, particularly in understanding the brain.
Moreover, the _notes folder houses comprehensive collections of markdown notes and cheat sheets across various courses covering computer science, statistics, and neuroscience topics, including computational neuroscience, causal inference, computer vision, linear algebra, and information theory.
Blog Posts
Following the tradition of sharing knowledge, the website also hosts a series of blog posts that address advancements in machine learning, statistics, and neuroscience. Some notable blog entries include:
- Paper Writing Tips (2023): Offers guidance and tips for writing academic papers.
- Forecasting Paper Titles (2022): Provides insights and tools for generating paper titles.
- imodels (2022): Discusses interpretable models, featured in a blog entry from the Berkeley Artificial Intelligence Research blog.
Additional Information
For those keen on receiving updates or exploring more about the project, the repository can be starred or followed on @csinva's Twitter. The site is built using several modern technologies including Jekyll, GitHub Pages, and the JupyterBook framework, ensuring a dynamic and interactive experience for its users.
Invitation to Explore
Chandan invites everyone to freely use these resources, hoping they serve as valuable tools for both learning and teaching. Whether you are looking to understand deeper aspects of machine learning or polish your research skills through detailed notes and presentations, csinva.github.io has something to offer.