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Machine Learning and Physics (MKTP6)
winter term 2025/2026
Lecturer: Bereau T, Hamprecht F
165 participants
Lecturer: Bereau T, Hamprecht F
165 participants
Course website
https://sciai-lab.org/teaching/25w/mlph/
Recommended textbooks
- Murphy, Probabilistic Machine Learning: An Introduction, https://probml.github.io/pml-book/book1.html
- Hastie, Tibshirani, Friedman, The Elements of Statistical Learning, https://link.springer.com/book/10.1007/978-0-387-84858-7
- Prince, Understanding Deep Learning, https://udlbook.github.io/udlbook/
- Bishop, Bishop, Deep Learning, https://link.springer.com/book/10.1007/978-3-031-45468-4
Requirements for admission to the exam
Threshold of 50% average points on the weekly exercises
Weekly exercises
- Due every Monday, 13:59
- Submit in pairs (i.e., groups of two)
- Upload on PhÜ one document per group of two
Curriculum (tentative)
Practice groups
- Group 1 (Lorenz Vogel)
23 participants - Group 2 (Christof Gehrig)
21 participants
INF 227 03.404, Tue 14:15 - 16:00 - Group 3 (Sander Hummerich)
21 participants - Group 4 (Antoine Petitjean)
19 participants - Group 5 (Gerrit Gerhartz)
20 participants - Group 6 (Roman Remme, Peter Lippmann)
22 participants - Group 7 (Ranit Das)
This tutorial will not be held.
32 participants
Phil 12 kHS, Fri 14:15 - 16:00
