Learn all the central concepts in machine learning and how to use AI coding agents to explore data, build machine learning models, validate performance, and accelerate domain-specific analysis.
A practical course for professionals who want to learn, evaluate, and apply modern machine learning in realistic domain contexts.
(Catering included)
The course is based on our highly successful machine learning course at DTU, which is attended by over a thousand students each year, and has been adapted to a professional audience with a focus on practical application and AI-assisted workflows.
The teachers are researchers with many years of experience teaching machine learning. Participants will also get access to our new book on Agentic Machine Learning.
Participants leave with a grounded way to judge when machine learning is useful, how to apply it responsibly, and how AI agents can support the analytical workflow.
Designed for professionals with existing domain knowledge who want to approach agentic machine learning and data science with confidence and realism.
No prior experience with machine learning or agentic AI is required. Participants should have knowledge of basic statistics and linear algebra. Key mathematical concepts will be introduced as needed. While programming will be done through AI agents, participants should be comfortable reading and evaluating code snippets in Python.
Participants are encouraged to bring example datasets or use cases from their own domain, alongside the course-provided datasets.
The course includes hands-on analysis, model-building, and AI-agent-assisted workflows, so participants should bring a laptop for the practical sessions.
The instructors have years of experience in teaching, researching, and applying machine learning. This knowledge has been distilled into this course and the accompanying book.
A postdoctoral researcher at DTU Compute focusing on statistical machine learning, tensor modelling, and data analysis for complex scientific data. He has extensive teaching experience across machine learning, statistics, software engineering, and applied data science, and brings extensive experience with state-of-the-art AI coding agents for accelerating workflows.
Professor of machine learning for the life sciences at DTU Compute with extensive teaching experience from introductory to advanced machine learning courses. He has been course responsible for many years for DTU Compute’s successful introductory machine learning course as well as served as head of studies for the AI and Data B.Sc. education.
The week moves from introductory machine learning and agentic coding to core machine learning concepts through supervised and unsupervised learning, ending with advanced agentic machine learning and future perspectives.
Contact Morten Mørup
Professor
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Course Coordinators
