Winter Term 2025/26
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All required information and all links to platforms for our courses are collected on this website. Please, do not write individual emails to the teachers, but use the online courses and platforms instead.
Bachelor:
Start of the course: For further information, see the Moodle course.
Content:
This course is offered in the form of a conference seminar. Similar to a scientific conference, participants submit their own conference papers, participate in the review of other papers, and meet at the end of the semester for a joint workshop where the results are presented and discussed. Thematically, this conference falls within the field of machine learning. The specific topics of the seminar papers will be announced by research assistants in the department and presented at the introductory event. This will take place at the beginning of the lecture period.
The introductory event is expected to take place in person. Current information on the course schedule can be found within the Moodle course.
If you are interested, please feel free to attend the introductory event (see Moodle).
Links:
Contact Person:
- Dominik Köhler
The material of the lecture will be taught according to the teaching concept "Flipped Classroom" in the form of videos. Accompanying the videos, there will be weekly live sessions in which Prof. Sick will discuss further questions with you to deepen your understanding. In addition, there is a short summary of the material from the previous week and a preview of the material for the coming week.
The exercise sheets are provided every Monday. The solutions are uploaded one week later after the last exercise. Only presence exercises are offered in which the exercises can be discussed.
All further information and regular announcements regarding the lecture and the test for exam admittance can be found in the corresponding Moodle course.
Contact person: Lukas Lührs
Students will develop a broad understanding of the signal processing chain in intelligent technical systems. Upon achieving the course objective, they will be able to competently tackle and solve simple tasks at the interface between data acquisition and processing, basic machine learning methods, and system evaluation—both independently and as part of a team.
Contact person:
Name: Benjamin Herwig
Email: herwig@uni-kassel.de
Master:
Start of the course: For further information, see the Moodle course.
Content:
This course is offered in the form of a conference seminar. Similar to a scientific conference, participants submit their own conference papers, participate in the review of other papers, and meet at the end of the semester for a joint workshop where the results are presented and discussed. Thematically, this conference falls within the field of machine learning. The specific topics of the seminar papers will be announced by research assistants in the department and presented at the introductory event. This will take place at the beginning of the lecture period.
The introductory event is expected to take place in person. Current information on the course schedule can be found within the Moodle course.
If you are interested, please feel free to attend the introductory event (see Moodle).
Links:
Contact Person:
- Dominik Köhler
The student can explain various models and algorithms in the field of deep learning; develop new modeling approaches for a wide variety of tasks such as classification and regression, object detection, etc.; independently plan and implement new applications; critically examine, compare, and evaluate existing methods and applications; and independently explore the latest techniques from the literature.
Contact person:
Denis Huseljic
The lecture covers the foundations of pattern recognition from a probabilistic point of view. The following topics are discussed:
- Basics (e.g., stochastics, model selection, curse of dimensionality, decision and information theory)
- Distributions (e.g., multinomial, Dirichlet, Gaussian and Student distributions, nonparametric estimation)
- Linear models for regression
- Linear models for classification
- Neural networks
Kernel methods
Contact: Huseljic, Denis
E-Mail: dhuseljic[at]uni-kassel[dot]de
The topics under consideration include, for example, (semi-)autonomous learning methods such as deep reinforcement learning, active learning, self-supervised learning/representation learning, collaborative learning, and continual learning, as well as physics-informed networks, temporal data analytics, and others.
Contact: Christoph Scholz