The course consists of five days (Mon-Friday) of lectures and exercises on key topics in machine learning. This years summer school will focus on several different advanced topics within machine learning pertaining to implementing models and analyzing data using machine learning tools in Python. The course (2.5 ects point) is passed by handing in a small report on one of the topics covered in the course. The exercises will cover both theoretical, technical programming and application aspects. It will be up to the students to decide on what aspects to focus on in the report. Specific machine learning application examples are used throughout the entire week. For further course details click here.
Technical University of Denmark, Building 421 Auditorium 73
9:00AM -4:20PM every day August 20th-24th, 2018
Lectures will be given by invited speakers and staff at the Section for Cognitive Systems.
Philip Johan Havemann Jørgensen Introduction to the Scikit-learn Ecosystem
Franciszek Olaf Zdyb Introduction to auto-sklearn
Adam Paszke Introduction to PyTorch
Aki Vehtari Introduction to PyStan
Ole Winther Introduction to Deep Learning with PyTorch
Søren Hauberg Diffeomorphisms for Dummies and Augmentations for Awesommies
Rasmus Bonnevie Stochastic Variational Inference
General understanding of machine learning, statistical modeling, mathematics and computer science. Programming experience in Python. For the course you are required to bring your own laptop computer.
Registration is no longer open email@example.com with your CV (maximum 2 pages). The CV has to be received no later than 14th of June. We will notify applicants regarding acceptance to the summer school 21th of June. -->
For academics (masters and PhD students) there is no registration fee for the course. For all other participants a course fee of DKK 8250 will be charged. Participants are to cover all other costs such as food, accommodation, and travel expenses.
For practical information regarding transportation and accommodation click here.
2017 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
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