ML in Fundamental Physics – Overview
About the lecture
Time and placeMonday 16:00-18:00 (Theresienstr. 37, A348), Tuesday 10:00-12:00 (Theresienstr. 37, A348)
There will be no exercise class on Monday June 24th. Instead we have a lecture.
There will be no lectures on the following dates: 25.06., 01.07., 02.07.
Exercises will be Mondays 12.00-14.00 (A348) and are held by Daniel Klaewer (corrections by Philip Betzler).
Examination (oral exam) will be in the week starting July 29th. Please register with the following information (by July 12th): name, student-id (Matrikelnummer), your course of study, preferred dates, and constraints.
- Mehta, Bukov, Wang, Day, Richardson, Fisher, Schwab: A high-bias, low-variance introduction to Machine Learning for physicists (1803.08823)
- MacKay: Information Theory, Inference, and Learning Algorithms (CUP, free online version)
- Goodfellow, Bengio, Courville: Deep Learning (MIT Press, deeplearningbook.com)
- No knowledge of python is required, but it is helpful if you have heard about a for-loop, if statements at some point in your life.
- Willingness to get your hands on coding.
- Jupyter notebook for Exercise sheet 1: getting_started.ipynb
- Jupyter notebook for Exercise sheet 4: classifier_dataset.ipynb
Verantwortlich für den Inhalt: Dr Sven Krippendorf
The colourful past and dark side of galaxies unveiled through population-dynamics of their stars