MAT-443

In this course, students will learn to understand the mathematical foundations of deep learning and neural networks. They will acquire practical skills in implementing and training deep models using the PyTorch framework. Additionally, they will be able to apply these methods to solve real-world problems.

These competencies can significantly facilitate students’ future careers in both science and industry, enabling them to apply deep learning methods effectively in a wide range of problems and innovations.

Benotete Modulprüfung.

Als Zulassungsvoraussetzung ist folgende Studienleistung zu erbringen:

1) Completion of exercises (including both theoretical and practical tasks) 2) Participation in competitions (e.g., constructing and training a neural network, approximating a function, implementing a graph) 3) Final project or written exam assessing theoretical foundations and basic implementations of neural networks

Modulprüfung: mündliche Prüfung (ca. 30 Minuten).

Students are expected to have a solid understanding of Linear Algebra and Numerical Methods I, as well as a basic or intermediate knowledge of Python.

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.

1. Wahlpflichtmodul für Bachelor Mathematik, Bachelor Technomathematik, Bachelor Wirtschaftsmathematik, Master Mathematik, Master Technomathematik
2. Angewandte Mathematik