| Section for Cognitive Systems |
| DTU Compute |
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The course consists of five days (Monday-Friday, 9 AM - 4 PM) of lectures and exercises. The lectures will cover theoretical and practical aspects of prominent intersections between Physics and Machine Learning. 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 handing in a small report on one of the topics covered in the course. For further course details click here.
The course content will mainly be related to the following three intersections between Physics and Machine Learning:
Physics informed ML Models - e.g. ML used to improve physics modelling or simulation.
Physics inspired ML Models - e.g. idea from physics used in building novel ML models.
Optimizing Physical Experimentation using ML - e.g. ML to learn efficiently in the real (or simulated) world.
Technical University of Denmark, lecture hall TBA
Monday:
Jesper L. Hinrich, DTU Compute, Introduction to Machine Learning in the Natural Sciences
Tuesday:
Jes Frellsen, DTU Compute, Energy based ML Models and Diffision
TBA, Generative AI
Wednesday:
Allan Ensig-Karup, DTU Compute, Surrogate Models and Physics Informed Neural Networks.
Mikkel N. Schmidt, DTU Compute, Surrogate Models and Graph Neural Networks.
Thursday:
Michael Riis Andersen, DTU Compute, Optimal Design through Bayesian Optimization
Friday:
TBA
TBA
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 registrer please send a CV to jehi@dtu.dk no later than the 14th of June 2024.
For academics (masters and PhD students) there is no registration fee for the course. PhD students outside of DTU have to further register here.
For all other participants a course fee will be charged and apart from signing up additional registration must be completed here.
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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