| 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, Building 324, Lecture hall 060. (Address: Richard Petersens Plads 324, 2800 Kgs. Lyngby, Denmark)
Monday:
Jesper L. Hinrich, DTU Compute, Introduction to Machine Learning in the Natural Sciences.
Jes Frellsen, DTU Compute, Introduction to Generative Modelling.
Tuesday:
Jes Frellsen, DTU Compute, Energy based ML Models .
Catherine Higham, School of Computing Science, University of Glasgow, Glasgow, UK, Diffusion Models and Generative AI.
Summer School Dinner at 6 PM.
Wednesday:
Allan Peter Engsig-Karup, DTU Compute, Physics-informed Machine Learning.
Mikkel N. Schmidt, DTU Compute, Graph Neural Networks.
Thursday:
Michael Riis Andersen, DTU Compute, Optimizing black-box functions using Bayesian optimization.
Tanya Levingstone, School of Mechanical and Manufacturing Engineering, Dublin City University, Dublin, Ireland, TBA
Friday:
Michael Kastoryano , Dept. of Computer Science at University of Copenhagen and AWS Center for Quantum Computing, Tensor Networks and Quantum Computing.
Jesper L. Hinrich, DTU Compute, Where do to go from here? Learnings, Key points, and Practical Information.
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. Confirmations will be sent out by the 28th of June .
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.
This course is funded by the Danish Data Science Academy.
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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