Artificial Intelligence (B. Sc.)

This introductory project offers a practical introduction to artificial intelligence and academic work. Students research and present AI-related topics, acquire the basics of academic research and learn how to use library resources. Working in teams, they build and train a simple AI model, run it and visualise the results. In addition, learning strategies and time management skills are taught.

This module introduces the fundamentals of programming using Python. Topics include data types, control structures, functions, modules, data structures, and basic software development and test-driven development concepts. In the lab, students apply their knowledge to practical tasks, including the development of a simulated drone autopilot with navigation, route planning, control, and data visualization features.

This module introduces the fundamental concepts of computer science and computer architecture. Topics include algorithms, computability, decidability, and complexity theory, as well as the representation of information in computer systems. Students also explore digital circuits and modern computer architectures, ranging from the von Neumann architecture to multicore systems, caching, instruction pipelining, and graphics processing units.

This module provides mathematical foundations for computer science and artificial intelligence. Topics include propositional and predicate logic, proof techniques, limits and continuity, as well as differential and integral calculus. Students also study Taylor polynomials and Taylor series, which are essential tools for modeling, analysis, and approximation in mathematics and engineering.

This module introduces the fundamentals of probability theory and statistics. Topics include descriptive data analysis and visualization, probability models, random variables and distributions, as well as regression and correlation analysis. In practical exercises, students apply these methods to domain-specific datasets using Python and deepen their understanding of statistical reasoning and data analysis.

This module introduces the fundamentals of scientific research and academic work. Students learn research methods, the structure and preparation of scientific papers, and the planning and management of research projects. The course also develops skills in documenting, presenting, and communicating scientific results and prepares students for independent research and thesis projects.

This module introduces object-oriented programming using Java. Topics include classes, inheritance, polymorphism, dynamic data structures, generics, interfaces, exceptions, and concurrent programming. Students also learn how to develop graphical user interfaces with JavaFX. In the lab, they implement a media player while applying modern software development practices, automated testing, and JUnit-based validation.

This module provides advanced foundations of computer science with a focus on operating systems and computer networks. Topics include processes and threads, memory management, file systems, device drivers, and input/output mechanisms. Students also study networking concepts, communication protocols, and layered architectures ranging from Ethernet and WLAN to TCP/IP-based applications and Internet services.

This module provides advanced mathematical foundations for computer science and artificial intelligence. Topics include linear algebra with vector spaces, matrices, eigenvalues, and linear transformations, as well as multivariable differential and integral calculus. Students learn to analyze multidimensional mathematical models and apply quantitative methods to complex technical and data-driven problems.

This module expands students' knowledge of statistical methods and probabilistic models for data-driven applications. Topics include stochastic processes, Markov chains, estimation methods, Bayesian statistics, Monte Carlo simulation, confidence intervals, and hypothesis testing. Students also gain an overview of regression models and analysis of variance, with practical exercises reinforcing the application of statistical techniques to real-world data.

This module introduces the fundamental methods of machine learning. Topics include supervised, unsupervised, and reinforcement learning, model evaluation, data preparation, and techniques for preventing overfitting and underfitting. Students explore classical methods such as k-nearest neighbors, regression, and classification, as well as neural networks, training procedures, and dimensionality reduction techniques for data-driven applications.

This module introduces the foundations and methods of mathematical optimization. Topics include various classes of optimization problems, their computational complexity, and the mathematical principles underlying optimization techniques. Students learn selected optimization algorithms, relevant data structures, and modern optimization tools and apply them to solve practical optimization tasks.

This module introduces methods for natural language and speech processing. Topics include language and text models, audio processing, statistical approaches, and neural networks for language technologies. Students explore applications such as text analysis, machine translation, language understanding, speech recognition, speech synthesis, and conversational systems including dialogue agents and chatbots.

This module introduces the foundations and methods of deductive systems in artificial intelligence. Topics include logic programming with Prolog, knowledge representation, inference mechanisms, constraint satisfaction problems, and automated reasoning. In practical exercises, students develop executable programs for solving logic-based problems and deepen their understanding through hands-on applications and case studies.

This module introduces the ethical and legal foundations of artificial intelligence. Topics include ethical theories, human–machine interaction, algorithmic bias, transparency, and explainability of AI systems, as well as their societal impact. Students also explore legal frameworks, data protection, copyright, and liability issues through practical examples of AI-based applications.

This module deepens students’ knowledge of machine learning with a focus on deep learning and unsupervised learning. Topics include neural network architectures, training and optimization methods, regularization, and hyperparameter tuning. Students also explore clustering techniques, autoencoders, and generative adversarial networks and apply modern approaches to the analysis of complex datasets.

This module combines project management and software engineering methods across the software development lifecycle. Students learn traditional and agile approaches, requirements analysis, software architecture, implementation, and testing techniques. Practical exercises cover project planning, risk and stakeholder management, UML modeling, and the application of agile methods such as Scrum.

This module introduces methods of computer vision and machine-based image understanding. Topics include classical image processing, feature extraction, image analysis, and 3D reconstruction, as well as modern deep learning approaches. Students learn techniques for classification, object detection, segmentation, pose estimation, and video analysis using frameworks such as PyTorch, TensorFlow, and Keras.

This module introduces the foundations of big data technologies and architectures for storing, managing, and processing large-scale datasets. Topics include relational and NoSQL databases, data warehouses, distributed data management, and scalability concepts. Students explore technologies such as Hadoop and gain experience in data integration, data governance, and distributed data processing.

This seminar explores current topics in artificial intelligence through independent scientific study. Students review relevant literature, structure complex subject areas, and present their findings in both a presentation and a seminar paper. Through academic discussion and critical reflection, they strengthen their skills in research, analysis, argumentation, and scientific communication.

This preparatory internship seminar supports students in preparing for their internship semester. The course focuses on reflecting on personal expectations, strengths, and areas for development while addressing uncertainties related to professional environments. Through group exercises, role-playing activities, and feedback methods, students strengthen their communication, conflict resolution, teamwork, and professional interaction skills.

The internship enables students to apply and deepen their knowledge of artificial intelligence in a professional environment. Students work in a company in Germany or abroad on practical AI-related tasks and projects, independently take responsibility for defined work packages, and gain insight into real-world AI applications. Their activities, experiences, and results are documented and reflected upon in an internship report.

This post-internship seminar supports the reflection and evaluation of experiences gained during the internship semester. Students present their activities, findings, and results in short presentations followed by group discussions. By connecting practical experience with theoretical knowledge and engaging in collaborative reflection, they further develop their professional, social, and communication skills.

This module deepens students’ knowledge of advanced machine learning methods. Topics include kernel methods, support vector machines, decision and regression trees, random forests, and ensemble techniques. Students also explore reinforcement learning and explainable AI approaches. In practical exercises, these methods are implemented and applied to real-world datasets using Python.

This module introduces concepts and methods of distributed artificial intelligence. Topics include parallel programming, scalability, distributed neural network training, and data and model parallelism. Students also explore federated learning, multi-agent systems, game-theoretic approaches, and swarm intelligence. The focus is on developing scalable, collaborative, and distributed AI solutions.

This module introduces methods and technologies for processing and analyzing large-scale datasets. Topics include data visualization, advanced SQL, distributed data processing, batch and stream processing, and cloud databases. Students also learn MLOps concepts and modern frameworks such as Spark. Practical assignments provide hands-on experience with current big data technologies and real-world use cases.

In this project module, students work in teams on a semester-long project in the field of artificial intelligence. Projects are often conducted in cooperation with companies or research institutions. Students take responsibility for planning, organization, and execution while applying either traditional or agile project management methods. The module strengthens technical, methodological, organizational, and communication skills through real-world AI challenges.

This module introduces the foundations and methods of IT security for modern information systems. Topics include cybersecurity threats, cryptography, network security, secure systems, malware, security principles, and security management. Students learn about common software vulnerabilities, risk assessment methods, AI-specific security challenges, and the use of artificial intelligence for detecting and mitigating cyber threats.

This module introduces the fundamentals of business administration and entrepreneurship. Topics include organization, marketing, finance, accounting, investment analysis, innovation management, and business model development. Students explore entrepreneurship and intrapreneurship concepts and apply them in a startup project by developing product ideas, business models, and marketing strategies.

This seminar systematically prepares students for the completion of their bachelor’s thesis. Topics include academic standards, research and documentation techniques, and examination regulations. Students identify and refine a suitable topic, develop a timeline and outline, coordinate their approach with their supervisor, and prepare the formal registration of their bachelor’s thesis.

The bachelor’s thesis represents the final academic achievement of the degree program. Students independently address a challenging problem within their field using scientific methods. They apply subject-specific knowledge, manage and organize their project, develop and evaluate solution strategies, and document their findings in a structured academic thesis. Depending on the topic, the results may also be presented and discussed.