T·01
Fundamentals
SVMs, logistic regression, decision trees, kernels: the classical core, derived by hand. This is the math everything else stands on.
Solve machine learning problems across three rounds, from the basics to current research, and compete with students from all over the world.
IMLC is a competition about how machine learning really works. You do not need to be an expert to participate: the first round needs no more than curiosity, school math, and a drive to learn. Each round builds on the previous one, so digging deeper into the topics is rewarded! Starting from the fundamentals, you work your way up to cutting-edge machine learning research.
The problems are the lessons. Each round builds on the last and we guide you through the material to make you succeed in each round. You do not need a powerful computer or a paid course.
Each round decides who advances, and the final ranking earns gold, silver and bronze awards. Participants come from over 190 countries.
What you learn here lays the groundwork for studying or working in machine learning, and for understanding the systems that will soon be part of everyday life.
Solutions for the qualification round are due by Friday, 6 November 2026.
no GPUs required · open to students worldwide · financial aid available
Six areas, one spectrum: the fundamentals that never change and the research that changes every month. Every round draws on all of it.
SVMs, logistic regression, decision trees, kernels: the classical core, derived by hand. This is the math everything else stands on.
How machines actually learn: gradient descent, loss landscapes, regularization, and why models that merely memorize fail to generalize.
Backpropagation by hand, then the architectures that took over: CNNs, RNNs and the transformer, understood layer by layer.
The current research edge: LLMs, fine-tuning, RAG, and the efficiency work, from quantization to distillation, that makes them deployable.
Where it all lands: vision, healthcare, recommender systems, science. The same methods, translated into systems people rely on.
Adversarial attacks, robustness, alignment and fairness: how models fail, how they get attacked, and what responsible deployment demands.
Each round raises the stakes: first the fundamentals, then a real research paper, then one final exam where everything comes together. See the full rounds & process →
Start here. A set of questions on the core ideas behind machine learning, from how models make decisions to how they learn from data. Score 15/17/20 points by age group to move on to the next round.
You receive a recent research paper on a topic in machine learning. Study it, then sit a supervised 60-minute exam: questions about the paper, plus two calculation problems. Registration is 12 EUR, and financial aid is available for anyone who needs it.
One timed exam of 30 questions that brings everything together: the foundations, the modern models, and the judgement to reason about both. The top finishers earn awards. Results announced 23 January 2027. See all awards →
Register your students, follow their progress, and put your school on the leaderboard. Partner schools get reduced registration for every participant.
Manage and track your students through every round, from qualification to the final exam, in one dashboard.
Register as a teacher →Partner schools receive reduced registration fees for all students and a certificate shipment to the school.
Become a partner school →Posters, problem sheets and training material to introduce machine learning thinking to your students.
Get the materials ↓Ambassadors bring the competition to their schools, universities and communities. You spread the word, mentor newcomers, and grow a local ML community. We support you with materials, a certificate and an international network.
Share the competition at your school or university and on local channels. Every registration through you counts toward your ambassador level.
Help participants in your region with questions about rounds, registration and preparation.
Advance through ambassador levels, earn certificates and references, and join an international network of ML enthusiasts.