About the International Conference on Machine Learning & Applied Data Science
ICMLADS 2027 takes place from 18 to 20 March 2027 in Berlin, Germany. It brings together researchers, graduate students and practitioners around a simple idea: machine-learning methods and the applied data science that puts them to work belong in the same conversation.
The scope is intentionally wide — core ML and deep learning, NLP and LLMs, computer vision, data engineering and MLOps, reinforcement learning, responsible AI, and applied data science across healthcare, finance, industry, energy and the public sector. If your work sits near this scope but isn't on the list, you're still encouraged to submit — the tracks help you find your place, not fence you out.
Eight tracks organize the programme, each opening onto a broad set of sub-topics so specialists and generalists alike have a home.
One presentation each edition is recognised for the clarity, originality and impact of the work and the way it's delivered.
How it's judged
Assessed on the practical and scholarly value of the research — what it adds and how well it addresses a real problem — combined with peer feedback from attendees and the organizing committee's final assessment.
What the winner receives
Waived registration for the next edition
A 20% discount on any other conference by the same team within 12 months
A registration discount for up to 10 colleagues for one conference within 12 months
The opportunity to publish articles on the organizer's official blog
Berlin is one of Europe's densest concentrations of research institutes, universities and data-driven companies — a fitting backdrop for a conference about turning method into application. The venue, INNSiDE by Meliá Berlin Mitte, sits in the centre of the city, two minutes from the Naturkundemuseum U-Bahn and a short walk from Berlin Hauptbahnhof, with Museum Island and historic Mitte on the doorstep.
Present your research or attend as a listener — either way, you leave Berlin with sharper work, new collaborators and a clearer view of where the field is going.