| Section for Cognitive Systems |
| DTU Compute |
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The course consists of five days (Monday-Friday) of lectures and exercises. The lectures will cover theoretical and practical aspects of Graph Representation Learning. Technical aspects, programming and application of the developed 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.
Monday: Shallow Graph Representation Learning
Tuesday: Diffusion and Factorization Based Graph Representation Learning
Wednesday: Graph Neural Networks I
Thursday: Graph Neural Networks II
Friday: Geometric Representation Learning
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 register please send an email to Anne Ringsted annri@dtu.dk with your CV (maximum 2 pages). The CV has to be received no later than 19th of June. We will notify applicants regarding acceptance to the summer school early July
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 of DKK 8250 will be charged and apart from signing up additional registration must be completed here.
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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