EAI-Math-Reading-Group

Machine Learning already relies on a lot of math, and with more and more researchers starting to bring mathematical tools from pure mathematics and physics into the field, many practitioners are starting to feel like they need to level up. Similarly, many people wanting to get started into alignment find themselves wishing they knew more advanced math.

If the previous paragraph sounds just like you, or you just like to learn math in general, consider joining this reading group. We usually meet on Sundays at 1PM Pacific on the EAI Discord server.

On discord, you can reach out to genetyx8 or join the thread.

Recordings of the meetings can be found on the EleutherAI Youtube channel

See the resources folder for the slides and links to the documents we read as part of the reading group.

Organisation

Since there are many interesting topics, and sticking for months to a single one is difficult, our current plan is to have sessions on long-running topics like Geometric Deep Learning interspersed with some shorter ones.

While we'll generally try to stay close to ML, some topics need a basic introduction, so we'll have some of those as well.

Contributing

• If there are particular topics you'd like us to cover, feel free to let us know or create an issue on this repo, and we'll add them to the list.
• If you are knowledgeable in some topic, and/or are motivated to present one or more sessions on it, we'd love to have you. Just open an issue on this repo on the topic, or reply to the existing issue for that topic.

Guidelines for contributors

In order to prepare sessions on a topic, it's important to have some source material available online that attendees can read. You can find some here, but there's plenty more out there.

A topic will be considered ready to be covered once it has reading material, people willing to present and an idea of how many sessions it should take, at which point it will be scheduled.

List of suggested topics

• Representation Theory
• Geometric Deep Learning
• Random Matrix Theory
• Partial Differential Equations (Theory + ML applications)
• Scientific Machine Learning
• Undergraduate Physics (Electromagnetism, Statistical Mechanics, Quantum Mechanics)
• Advanced Linear Algebra
• Dynamical Systems
• Topology
• Measure Theory
• Game Theory
• Information Geometry
• Functional Data Analysis
• Topological Data Analysis