International Machine Learning Competition

Take on three rounds of machine learning problems, advance from core ideas to current research, and compete with students from around the world.

190
Countries
$1,500
Prize money
3
Rounds
fig. 1 Support Vector Machine
maximum-margin classifier
About

Learn the ideas.
Put them to the test.

IMLC is an international competition for students who want to understand machine learning and see how far their thinking can take them. We teach the ideas you need, then challenge you with problems that grow harder in each round. You can start with no experience and end up solving problems you never thought you could.

  • a.

    Build the skills to compete

    We teach machine learning from the ground up. You learn why the ideas work, not just which button to press.

  • b.

    Take on harder problems

    Each round asks more of you. Clear thinking matters more than expensive hardware, and every problem gives you a chance to improve.

  • c.

    See how far you can go

    Advance from the foundations to a recent research paper and a final exam, while testing your skills against students worldwide.

Learn the ideas, solve the problems, and earn your place in the next round.
A real question from the competition
How two attempts to train a model settle into different solutions.

The two curves show a model learning. Which attempt will handle new, unseen data better, and how can you tell just by looking? By the end of the competition, reading a picture like this will feel natural.

No experience required to start · we teach you along the way
Participate

The clock is already running.

Solutions for the qualification round are due by Friday, 6 November 2026.

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Download Problems Submit Solutions

no GPUs required · open to students worldwide · financial aid available

The spectrum

From SVMs to state of the art.

Six areas, one spectrum: the fundamentals that never change and the research that changes every month. Every round draws on all of it.

T·01

Fundamentals

SVMs, logistic regression, decision trees, kernels: the classical core, derived by hand. This is the math everything else stands on.

svm · logistic regression · kernels
T·02

Optimization & Generalization

How machines actually learn: gradient descent, loss landscapes, regularization, and why models that merely memorize fail to generalize.

gradient descent · bias–variance · overfitting
T·03

Deep Learning

Backpropagation by hand, then the architectures that took over: CNNs, RNNs and the transformer, understood layer by layer.

backprop · cnn · transformers
T·04

Frontier Models

The current research edge: LLMs, fine-tuning, RAG, and the efficiency work, from quantization to distillation, that makes them deployable.

llms · rag · distillation
T·05

Applications

Where it all lands: vision, healthcare, recommender systems, science. The same methods, translated into systems people rely on.

vision · healthcare · recommenders
T·06

Trustworthy AI

Adversarial attacks, robustness, alignment and fairness: how models fail, how they get attacked, and what responsible deployment demands.

adversarial · alignment · fairness
Rounds

Three rounds over five months.

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 →

R1
Qualification until 6 November 2026

Qualification Round

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.

online · solo · from home
R2
Pre-Final December 2026

Pre-Final Round

You receive a recent research paper on a topic like LLM efficiency, vision transformers or adversarial robustness, and answer questions that test whether you truly understood its method, its assumptions and its limits. You have five days. Registration is 12 EUR, and financial aid is available for anyone who needs it.

research paper · reading · written
R3
Final Sunday, 17 January 2027

Final Exam

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 →

supervised · timed · awards
Teachers & Schools

Bring the IMLC to your classroom.

Register your students, follow their progress, and put your school on the leaderboard. Partner schools get reduced registration for every participant.

Teacher Account

Manage and track your students through every round, from qualification to the final exam, in one dashboard.

Register as a teacher →

School Partnership

Partner schools receive reduced registration fees for all students and a certificate shipment to the school.

Become a partner school →

Classroom Materials

Posters, problem sheets and training material to introduce machine learning thinking to your students.

Get the materials ↓
Ambassadors

Represent the IMLC in your country.

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.

  • a.

    Promote

    Share the competition at your school or university and on local channels. Every registration through you counts toward your ambassador level.

  • b.

    Support

    Help participants in your region with questions about rounds, registration and preparation.

  • c.

    Grow

    Advance through ambassador levels, earn certificates and references, and join an international network of ML enthusiasts.

Newsletter

Stay in the loop.

Round openings, deadlines and results: a handful of emails a year, nothing else.