[Lecture] Advanced Machine Learning
In the lecture we will cover various advanced machine learning concepts, techniques, and algorithms. We will place focus both on the mathematical and theoretical aspects, as well as the practical aspects which involve implementing, training, and optimizing machine learning models using real-world datasets. Some of the topics that we will cover include: semi-supervised learning, machine learning for graph data, machine learning for sequential data, Gaussian processes, temporal point processes, trustworthy machine learning, advanced topics in optimization, machine learning theory, and generative models.
[Lecture] Deep Learning
New developments in the field of deep neural networks have enabled a large amount of applications in recent years that were previously unthinkable to achieve in high quality: From image recognition, speech recognition, simulation of protein folding, automatic playing of computer games to image generation or speech generation with systems like ChatGPT. In this lecture we will cover the concepts behind deep neural networks, in particular Convolutional Neural Networks, attention mechanisms, modern transformer architectures, and their extensions. We focus on supervised deep learning and unsupervised deep learning, where many training examples are available for a given task, and the network learns independently from this data. In this lecture, a mathematical understanding of the mechanisms and the design of neural networks is conveyed. At the same time, examples are given on how to efficiently implement and use neural networks with the help of modern Deep Learning frameworks such as PyTorch.
[Lecture] Scientific Machine Learning
Scientific Machine Learning is a new research area that is developing as a field of its own, in which techniques of scientific computing and machine learning are combined and further developed. This results in hybrid methods that are applied to the discretization of partial differential equations, the development of fast and robust solvers, and new modeling techniques.
Keywords are
- Domain-aware scientific machine learning,
- Mathematics-informed machine learning,
- Interpretable scientific machine learning,
- Machine learning-enhanced simulations,
- Hybrid modeling (machine learning + first principle modeling).
[Seminar] On Applications in Life Sciences
The seminar will discuss recent work on applications of mathematical methods in the life sciences. The focus is on current developments of machine learning and artificial intelligence methods for industrial problems in the fields of pharmaceuticals and agricultural sciences. This seminar discusses different aspects, such as the mathematical methodology behind each method, its computational complexity, and possible applications. In individual cases, publicly available methods will also be applied and the results discussed.
[Seminar] Machine Learning
In this seminar, we will discuss machine learning methods currently used in various application areas. We will focus on concrete methods, for example:
• mathematical basics,
• model-selection algorithms,
• regularization,
• dimension reduction,
• decision trees,
• support vector machines,
• neural networks.
The basis for the seminar is the book “The Elements of Statistical Learning”.
[Seminar] Theory of Machine Learning
In this seminar the theoretical and algorithmic basics of machine learning will be treated. Individual chapters from the book “Understanding Machine Learning: From Theory to Algorithms” are discussed.
[Practical Course] Applied Visual Analytics
The practical training deals with the design, implementation and evaluation of visual analysis of large and complex data sets: Visualization, interaction, human perception, data analysis and their combination to solve application-oriented problems. Problems from current research and application topics in the field of visual analytics will be addressed and implemented. Application areas are for example finance, economics, geosciences, meteorology, medicine, biology, transportation, or sports. In addition to deepening technical knowledge, the course can also be used to acquire communication and presentation skills.