Resources

Generating Research Project Ideas

Finding a research idea is usually the hardest part of the whole process. This guide walks through narrowing your focus, reading what others have done, figuring out what they haven't, and asking whether you can do something about it.

How to Design Experiments

Most students arrive at experimental design backwards. This guide is about developing a clear account of what each experiment is supposed to show before the experiments start, which saves time and produces research that is easier to write up and defend.

How to Find ML Research Opportunities

This guide covers how to find research opportunities, not how to pick a research topic or how to write up your results. SAIRC has separate resources for both of those.

How to Get Free Compute

If you're doing AI research as a student, you're going to need compute. Between notebook environments, cloud credits, and serverless platforms, you can get pretty far without spending a dollar. This page collects the best options we know of.

How to Learn PyTorch

PyTorch is the framework that most ML researchers write their code in. If you want to do ML research, reproduce published results, or read someone else's training script and understand what every line does, you need to know this framework.

ML Forums & Communities

A lot of how the field actually moves happens in conversations that never make it into papers. Understanding what people actually think about a topic usually means spending time in the communities where those conversations happen.

How to Learn Machine Learning

Machine learning sits at the intersection of computer science and mathematics. The CS side gives you the tools to build things; the math side gives you the language to understand what those tools are actually doing.

Machine Learning Opportunities for Students

Competitions, hackathons, and challenge programs are all ways to get experience where the outcome is uncertain. This resource lays out the main options available to students, with an honest sense of what each one is and is not good for.

Beginner & Intermediate ML Projects

The fastest path from 'I know some Python' to genuine ML competence is building things. The twelve projects in this guide span tabular data, computer vision, NLP, time series, and generative modeling.

How to Publish Machine Learning Research

You finished a research project. Now what? This guide covers the main avenues for getting your ML research out into the world. The right choice depends on what kind of work you did, how polished it is, and what you want out of the process.

Summer Programs in Machine Learning

Summer is one of the best windows a student has for serious work. For students interested in AI research, most of these programs offer real mentorship and a chance to see what research actually looks like before committing to it in college.

How to Write a Machine Learning Paper

Most student researchers treat writing as something you do after the research is done, but that instinct is backwards. Writing is the process that actually sharpens your ideas into something defensible.