Machine learning is behind most of what is called AI today. Everyone uses these systems, but few people learn how they actually work: how a model is trained, why it makes the predictions it does, and when it breaks. That understanding is what the International Machine Learning Competition is about. Students solve machine learning problems across three rounds, from the basics to current research, and compete with students from all over the world. No prior experience is needed to start.

Vision, Mission, and Values

The International Machine Learning Competition aims to be accessible to all students from all countries regardless of their background, school, or institution. For that, the International Machine Learning Competition uses today's technologies and the global connection through the internet to make this competition possible: All you need is a pen, paper, and an internet connection to participate!

Using a tool and understanding it are two different things. Anyone can run a model. Understanding why it works takes mathematics, statistics, and programming, and that knowledge is what lets you judge when a result can be trusted and when it cannot. We want students to learn the how, not just the what: how a model learns from data, where it fails, and how to build something better. The students who understand these systems today are the ones who will design what comes next. Our goal is a program that gets students there early, and that gives students, parents, and teachers a simple way to take part.

What is unique about IMLC?

Although the competition format is integral to the IMLC, its primary goal is to encourage students to engage with machine learning and spark further interest in this and related fields. As a result, IMLC serves as a learning experience, motivating students and allowing them to learn through experimental problem-solving formats. Noteworthy aspects of the IMLC program include the following:

• Internationality:
  The International Machine Learning Competition is a global competition from the first round onward. All participants solve the same problems in every round: This enables a fair evaluation and allows for international exchange among participants, which is usually only available to a few national ambassadors participating in international rounds.


• Digital Accessibility:
  The competition uses the possibilities of the modern internet to give all students the opportunity to participate regardless of nation, region or school affiliation. Although having a teacher's support is beneficial, participating as an individual is always possible from anywhere around the world.


• Experimental Formats:
  Part of the IMLC experience are problem formats, which are uncommon for traditional competitions and aim for a distinctive learning experience. This includes problems that focus on the extension of school-typical formats through thought-provoking modifications or types of problems, which require a deeper dive into the research surrounding a topic.


• Real-World Research:
  Getting students in touch with real-world examples of research is an essential feature. The Pre-Final Round usually includes recently published research articles as part of the problem format, which is unique to IMLC: For many participants, this is the first time learning about actual machine learning research, and we offer additional support to expand this learning experience.


• Adjusted Difficulty:
  The problems are designed to appeal to both enthusiasts as well as students who are just starting to find interest in machine learning beyond the school curriculum. To ensure that everyone has a positive experience, the level of difficulty is adjusted to include more approachable problems similar to those typically seen in school, as well as more challenging problems that incorporate unusual formats.


• Online Tools for Teachers:
  We provide teachers and schools with supplementary resources and support them throughout each edition. Teachers have access to a dedicated interface for managing the submissions of their students, tracking their results & certificates and supervising the Final Round Exam.


• Local Groups & Ambassadors:
  As IMLC goes far beyond the annual competition, we strongly encourage and support the formation of local student groups related to machine learning & IMLC around the world, including meetups and other events. The IMLC Ambassador Program allows motivated students and mentors to encourage other youths to engage with machine learning and to establish a local community that brings together students over building and understanding models.


• Supportive Alumni Network:
  Our alumni network encourages international exchange, including the distribution of various relevant opportunities like scholarships or sponsored events, to support each participant's academic career.

How does IMLC compare to other competitions?

Comparing the International Machine Learning Competition to other competitions can be misleading: IMLC does not intend to be an alternative to established competitions or their national variations and actively encourages students to take part in such opportunities.

Instead, IMLC serves as a supplementary program designed to engage a wide range of students, particularly those just beginning to develop an interest in machine learning and related subjects. It focuses on accessibility, making it a platform for students of all skill levels to explore and grow their understanding of how machine learning models work.

Although the annual competition is the central element, IMLC is not primarily structured around competitiveness but instead prioritizes the learning experience. Through experimental problem formats and real-world research topics, the competition introduces students to thought-provoking challenges that extend beyond standard school curricula and differ from traditional competition content. This approach appeals to students with varying levels of expertise, offering both approachable problems and more advanced tasks that inspire curiosity and creativity.

Moreover, IMLC empowers students by providing an independent platform that does not rely on their school or country. It connects participants through a global network, encouraging international collaboration and exchange. With a strong alumni community and supportive resources for teachers and local groups, IMLC goes beyond being just a competition.

History, Organization, and Funding

The International Machine Learning Competition is coordinated by Edu.Harbour and takes place annually on an international level. The organizing team is constantly evolving, with most team members being based in Germany. The idea formed after the success of other international competitions coordinated by Edu.Harbour, with the aim of establishing a similar online competition addressing the fascinating world of machine learning.

The full organizing team of IMLC 2026 can be found on this page: Organizing Team of 2026 ↗

Organizing such an international competition involves significant costs, including human resources, technical infrastructure, materials, and more. As an independent and globally accessible program, IMLC is grateful to its sponsors for covering some of these expenses. The remaining costs are funded through participant contributions, such as the registration costs for the Pre-Final Round (Flyer) ↗.

As a growing competition, IMLC is continuously evolving and improving each year to enhance the experience for participants and expand its impact worldwide!