Introduction to Programming for Science Students
Lecturer: Ludwig Striet
Language: in German
Lecture: Mo, 16-18h, HS Weismann-Haus, Albertstr. 21a
Tutorial: 2 hours, various dates
none
Computer Exercise
Elective (Option Area)
Computer exercises in Numerics
Lecturer: Sören Bartels
Assistant: Vera Jackisch
Language: in German
In the practical exercises accompanying the Numerics II lecture, the algorithms developed and analysed in the lecture are implemented in practice and tested experimentally. The implementation is carried out in the programming languages Matlab, C++ and Python. Elementary programming skills are assumed.
See the lecture Numerics II.
In addition elementary programming knowledge.
Computer Exercise
Elective (Option Area)
Computer exercises in Statistics
Lecturer: Sebastian Stroppel
Language: in German
Mo, 14-16h, PC-Pool Raum 201, Hermann-Herder-Str. 10
This computer exercise course is aimed at students who have already attended the lectures Elementary Probability Theory I and II or are attending the second part this semester. Computer-based methods will be discussed to deepen the understanding of the lecture material and demonstrate further application examples. For this purpose, the programming language python is used. After an introduction to python, methods of descriptive statistics and graphical analysis of data will be considered, the numerical generation of random numbers will be explained and parametric and non-parametric tests and linear regression methods will be discussed. Previous knowledge of python and/or programming skills are not required.
Analysis I+II, Linear Algebra I+II, Elementary Probability Theory I+II (part II can be followed in parallel).
Computer Exercise
Elective (Option Area)
Lecturer: Carola Heinzel
Assistant: Samuel Adeosun
Language: in English
Do, 14-16h, PC-Pool Raum -100, Hermann-Herder-Str. 10
This course introduces the foundational concepts and practical skills necessary for understanding and implementing machine learning models, with a particular focus on deep learning and neural networks. Students will progress from basic programming skills in Python , with a focus on the PyTorch library, to advanced topics such as training multi-layer perceptrons, optimization techniques, and transformer architectures. By the end of the course, participants will have the ability to implement and analyze neural networks, apply optimization strategies, and understand modern transformer-based models for tasks such as text generation and time series analysis.
Basic knowledge in programming and basic knowledge in stochastics.
Computer Exercise
Elective (Option Area)
Lecturer: Peter Pfaffelhuber
Assistant: Sebastian Stroppel
Language: in English
Di, 12-14h, SR 404, Ernst-Zermelo-Str. 1
Lean4 is both, a programming language and an interactive theorem prover. By the latter, we mean software that is able to check mathematical proofs. It is interactive since the software tells you what remains to be proven after every line of code. The course is an introduction to this technique, with examples from various fields of mathematics. Lean4 is special since researchers all over the world are currently building a library of mathematical theories, which contains at the moment around 1.5 million lines of code. I aim to cover basics from calculus, algebra, topology and measure theory in Lean4.
Analysis 1, 2, Linear algebra 1
Computer Exercise
Elective (Option Area)