Details

Lecture contents

- Mathematical Basics of AI and Data Science- Linear Algebra
- Probability Theory and Statistics
- Optimization

- Introduction to Artificial Intelligence- Problem Solving and Heuristic Search
- Logic and Knowledge Representation
- Planning, Learning and Decision Making under Uncertainty

- Data Science and Machine Learning Fundamentals- Data Collection, Cleaning, Filtering
- Model Building
- Model Evaluation
- Definition of Machine Learning and classes of Machine Learning Algorithms
- Machine Learning Classifiers
- Evaluation of Machine Learning Algorithms

Assessment methods

Continuous assessment
- Project work
- Exercises during lectures
- Final written exam

Teaching methods

- Lecture
- Group work (project)
- Practical exercises
- Continuous Discussion and feedback

Language

English

Language

German

Lecture contents

- Independent work on a topic from the area of Computer Science, primarily based on the technical topics of the elective modules in the 4th and 5th semesters at a scientific level under the guidance of a supervisor.
- Elaboration of the bachelor thesis 1

Assessment methods

Final exam
Approbation of bachelor thesis

Teaching methods

Implementation of a project and elaboration as a bachelor thesis with coaching. Students present the current development of their bachelor thesis 1 at regular intervals and put it up for discussion.

Language

German

Lecture contents

Students apply the skills acquired to complete a project in a coordinated and structured manner.
project in a coordinated and structured manner. In doing so, they independently define a concrete sub-goal in the project. A well-founded theoretical approach is thus combined with practical application. Collaboration on an industrial R&D project or on current problems within the framework of the R&D activities of the UAS is possible.

Assessment methods

Final exam
Practical project in small groups.

Teaching methods

Group work, practical project implementation accompanied with exercises and coaching.

Language

German

Lecture contents

Virtual and augmented reality include methods for extending real-world content to provide contextual information through intelligent algorithms. Virtual and Augmented Reality are used in various fields, such as: medicine, industry, education, tourism and computer games. To enable Virtual and Augmented Reality, the methods of Artificial Intelligence and Machine Learning especially Deep Learning play a very important role. Virtual and Augmented Reality are important components of Cyber-Physical Systems, which in turn form the basis for digitalization and Industry 4.0.
The course covers in particular the following contents:
- Application examples of Virtual and Augmented Reality
- Requirements for Augmented Reality and Deep Learning applications
- Displays, cameras and other sensors for Virtual and Augmented Reality
- Calibration and filtering
- Computer vision algorithms for object and scene recognition
- Localization, tracking and navigation
- Deep Learning algorithms and network architectures, especially CNN and RNN
- Use of deep learning algorithms in virtual and augmented reality applications

Assessment methods

Final exam
Group work

Teaching methods

Lecture, hands-on exercises, case studies, discussion of current literature, implementation of algorithms and applications.

Language

English

Lecture contents

- Independent work on a relevant subject based on the technical topics of the elective modules and possibly the Bachelor thesis 1 at a scientific level under the guidance of a supervisor.
- Elaboration of the bachelor thesis 2

Assessment methods

Final exam
Approval of the bachelor thesis

Teaching methods

Carrying out a practical work and elaboration as a bachelor thesis with coaching. Students present the current development of their bachelor thesis 2 at regular intervals and put it up for discussion.

Language

German

Lecture contents

- Mathematical Basics of AI and Data Science- Linear Algebra
- Probability Theory and Statistics
- Optimization

- Introduction to Artificial Intelligence- Problem Solving and Heuristic Search
- Logic and Knowledge Representation
- Planning, Learning and Decision Making under Uncertainty

- Data Science and Machine Learning Fundamentals- Data Collection, Cleaning, Filtering
- Model Building
- Model Evaluation
- Definition of Machine Learning and classes of Machine Learning Algorithms
- Machine Learning Classifiers
- Evaluation of Machine Learning Algorithms

Assessment methods

Continuous assessment
- Project work
- Exercises during lectures
- Final written exam

Teaching methods

- Lecture
- Group work (project)
- Practical exercises
- Continuous Discussion and feedback

Language

English

Language

German

Lecture contents

- Independent work on a topic from the area of Computer Science, primarily based on the technical topics of the elective modules in the 4th and 5th semesters at a scientific level under the guidance of a supervisor.
- Elaboration of the bachelor thesis 1

Assessment methods

Final exam
Approbation of bachelor thesis

Teaching methods

Implementation of a project and elaboration as a bachelor thesis with coaching. Students present the current development of their bachelor thesis 1 at regular intervals and put it up for discussion.

Language

German

Lecture contents

Students apply the skills acquired to complete a project in a coordinated and structured manner.
project in a coordinated and structured manner. In doing so, they independently define a concrete sub-goal in the project. A well-founded theoretical approach is thus combined with practical application. Collaboration on an industrial R&D project or on current problems within the framework of the R&D activities of the UAS is possible.

Assessment methods

Final exam
Practical project in small groups.

Teaching methods

Group work, practical project implementation accompanied with exercises and coaching.

Language

German

Language

English

Language

German

Lecture contents

Software Engineering Project 1 enables students to implement the knowledge acquired during their studies in a concrete project. In the first semester, a concrete problem is analyzed and a design for the software solution is worked out using Advanced Project Management methods. This solution will then be implemented in the software project in the second semester.
The LV covers in particular the following contents:
- Application of modern project management methods to a concrete project Formulation
- , classification and prioritization of requirements for a concrete problem Use of
- UML diagrams (Use Case, class, activity and sequence diagrams) for software design design to meet requirements Structured
- and standardized documentation of results as a high-level design document that serves as a basis for implementation
.

Assessment methods

Module exam

Teaching methods

Group work, practical project implementation accompanied by exercises and coaching

Language

German

Lecture contents

Software Engineering Project 2 enables students to implement the knowledge acquired during their studies in a concrete project. In the second semester, based on requirements and design draft from the software design project, the software solution is implemented in the first semester using modern software development methods and tools. This implemented solution is then systematically tested and improvements are incorporated.
The LV covers in particular the following contents:
- Implementation of classes and data structures based on the High Level Design documentUse of
- software algorithms for the implementation of activity and sequence diagramsDefinition
- and prioritization of test cases under consideration of requirementsConduction of
- test scenarios with current test tools

Assessment methods

Module exam

Teaching methods

Group work, practical project implementation accompanied by exercises and coaching

Language

German

Lecture contents

Students work individually or in small groups on projects related to software design and software engineering technologies and applications in the context of university R&D activities or within the scope of their individual professional activities. These projects are the practice-relevant basis for the master theses.

Assessment methods

Continuous assessment
Project progress, proof of function, project presentation

Teaching methods

-

Language

German-English

Lecture contents

- Deepening the basic principles of scientific work
- Reading, understanding and interpreting relevant scientific texts
- Literature research
- Formal methods of scientific work
- Students present the current development of their Master's thesis at regular intervals and put it up for discussion in the plenum

Assessment methods

Continuous assessment
Presentations, home exercises

Teaching methods

Lecture, Case Studies

Language

German