- Nummer
- 011062, SS26
- Dozentinnen und Dozenten
- Veranstaltungstyp
- Vorlesung, 2+1
- Ort und Zeit
- M/1011 Do 12:00 2h
- Modul-Zugehörigkeit (ohne Gewähr)
- DPL:B:-:2 – Mathematik, Diplom (auslaufend)
- MABA:-:4:MAT-443
- DPL:A:-:- – Mathematik für andere Fächer (Service)
- DPL:F:-:1 – Mathematik für andere Fächer (Service)
- DPL:E:-:- – Mathematik, Promotionsstudiengang
- TMABA:-:4:MAT-443
- WIMABA:-:4:MAT-443
- MAMA:-:4:MAT-443
- TMAMA:-:4:MAT-443
- WIMAMA:-:4:MAT-443
- Sprechstunde zur Veranstaltung
- nach Vereinbarung
- Erforderliche Voraussetzungen
- Students are expected to have a solid understanding in Numerical methods I
- Inhalt
The course introduces students to programming in Python, numerical methods, and basic concepts of artificial intelligence. Students learn to implement algorithms such as solving linear systems, interpolation, and matrix decompositions in Python. In the final part, these skills are applied to simple deep learning models and practical tasks such as image recognition.
- Aktuelle Informationen
Students are recommended to have their own laptop with Python environments installed, preferably version 3 or higher, and a Python editor such as Jupyter Notebook, Visual Studio Code, or a similar tool. It is also desirable to have Git-related software installed.
- Bemerkungen
Link zum Modulhandbuch Mathematik
Link zu den Modulbeschreibungen im Service
- Empfohlene Literatur
- Numerical Python: Scientific Computing and Data Science Applications with NumPy, SciPy and Matplotlib (Second Edition) by Robert Johansson, Apress, 2018.
- Available online: https://jrjohansson.github.io/numericalpython.html
- Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville, 2016. Available online: https://www.deeplearningbook.org
- Algorithmic Mathematics in Machine Learning by Bastian Bohn, Jochen Garcke and Michael Griebel, 2024
