AI Research Resources for Students | SAIRC<br>For Researchers<br>Resources<br>Everything you need to go from idea to published paper. Free tools, compute, courses, and guides curated for AI researchers.<br>Get In Touch<br>Please email us if you have any specific research questions or requests that aren't addressed by these resources. We can help with mentorship requests, general research advice, and more! Reach us at sairc.support@gmail.com.

Generating Research Project Ideas<br>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.<br>GuideResearchIdeasGetting Started<br>How to Design Experiments<br>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.<br>GuideExperimentsResearchMethodology<br>How to Find ML Research Opportunities<br>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.<br>GuideOpportunitiesResearchML<br>How to Get Free Compute<br>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.<br>GuideComputeGPUFree<br>How to Learn PyTorch<br>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.<br>GuidePyTorchFrameworkDeep Learning<br>ML Forums & Communities<br>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.<br>GuideCommunityForumsNetworking<br>How to Learn Machine Learning<br>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.<br>GuideMachine LearningMathGetting Started<br>Machine Learning Opportunities for Students<br>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.<br>GuideOpportunitiesCompetitionsStudents<br>Beginner & Intermediate ML Projects<br>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.<br>GuideProjectsBeginnerIntermediate<br>How to Publish Machine Learning Research<br>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.<br>GuidePublishingResearchPapers<br>Summer Programs in Machine Learning<br>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.<br>GuideSummer ProgramsMentorshipResearch<br>How to Write a Machine Learning Paper<br>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.<br>GuideWritingPapersResearch<br>Open-Source AI/ML Projects to Contribute To<br>Contributing to open-source ML projects builds real skills and gets your name into codebases people actually use. This guide covers finding appropriate issues, navigating the pull request workflow, and a curated list of active projects where student contributions are realistic.<br>GuideOpen SourceContributingProjects<br>How to Properly Cold-Email Professors<br>Cold-emailing a professor is one of the most accessible paths to research mentorship, and one of the most commonly mishandled. Professors who mentor high school students care about demonstrated preparation and specific interest in their lab's work, not credentials. This guide is about getting that email right.<br>GuideCold EmailProfessorsResearch<br>Introduction to Subfields: Reinforcement Learning<br>Republished with permission by Devansh, writer of AI Made Simple. RL is one of the big 3 paradigms of ML Research (along with supervised and unsupervised learning). RL is based on teaching a model to maximize a reward function.<br>ExternalReinforcement LearningMachine...