Choose a learning path
Combine courses, workshops, documentation, and focused practice around the skills you want to build.
Explore courses →Learn by making
Student Learning & Opportunities
Build the practical skills, artifacts, and relationships that help you apply AI with confidence.
Build your portfolio
Turn assignments and experiments into clear project stories with code, evaluation, and lessons learned.
Research opportunities
Look for ways to contribute to faculty research, experimental projects, or interdisciplinary work.
See research →Internships & co-ops
Use practical projects and industry conversations to prepare for applied AI experience.
Connect with industry →Competitions & communities
Find a team, a challenge, or a student organization where you can practice in public.
Cloud, GPU & HPC access
Learn the pathways and responsible practices for using compute in your experiments.
View resources →Make evidence
A project is more than a demo.
Show what you tried, how you evaluated it, where it failed, and what you would do next. That is the difference between using AI and engineering with it.
- Write down the problem and intended user.
- Keep the smallest useful version working.
- Measure quality, cost, speed, and limitations.
- Publish code and a readable project note.
- Ask for critique from peers, faculty, or industry.
Start here
Bring one question to the next workshop.
Hands-on sessions are a good place to meet collaborators and turn a vague idea into a concrete next experiment.
Find a workshop