| Section for Cognitive Systems |
| DTU Compute |
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The course consists of five days of lectures, exercises, a hackaton, and presentations. The lectures will cover theoretical and practical aspects of Machine Learning Operations. Technical aspects, programming and application of the covered concepts will be explored in tutorials and exercises. The course (2.5 ECTS points) is passed by participating and presesent work done in the hackathon. For further course details click here.
Technical University of Denmark, Lecture hall TBA. (Address: Richard Petersens Plads, 2800 Kgs. Lyngby, Denmark)
Monday (9 - 16):
Nicki Skafte Detlefsen, DTU Compute, Introduction to Machine Learning Operations.
Speaker TBA, MLOps and Orchestration
Tuesday (9 - 16):
Speaker TBA , Federated Learning
Speaker TBA, ML in Resource Constrained Environments
Summer School Dinner at 18
Wednesday (9 - 16):
Speaker TBA, Embedded Systems and SBC
Speaker TBA, ML in Production
Thursday (9 - 20, Location TBA):
(9-10) Introduction to Hackathon
Friday (10 - 16):
(10-12) Hackathon finalized
(13-16) Group presentations and Exam
General understanding of machine learning, statistical modeling, mathematics and computer science. Programming experience, ideally in Python. For the course you are required to bring your own laptop computer.
To participate in the course, an application is required. Please send an application consisting of a CV to jehi@dtu.dk no later than the 21st of June 2025. The applications will be assessed and confirmations will be sent out by the 30th of June. If there are open spaces after the deadline, applications will be assessed on a rolling basis.
After revieving a positive confirmation on the application, registration in DTUs systems must be carried out. For academics (masters and PhD students) there is no registration fee for the course. Students affiliated with DTU can use the course planner to register. PhD students outside of DTU have register via here. For all other participants a course fee will be charged and apart from signing up additional registration must be completed here.
2024 version of 02901 Advanced Topics in Machine Learning
2023 version of 02901 Advanced Topics in Machine Learning
2022 version of 02901 Advanced Topics in Machine Learning
2021 version of 02901 Advanced Topics in Machine Learning
2020 version of 02901 Advanced Topics in Machine Learning
2019 version of 02901 Advanced Topics in Machine Learning 2018 version of 02901 Advanced Topics in Machine Learning2017 version of 02901 Advanced Topics in Machine Learning
2016 version of 02901 Advanced Topics in Machine Learning
2015 version of 02901 Advanced Topics in Machine Learning
2014 version of 02901 Advanced Topics in Machine Learning
For further information, please contact:
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