AI Systems Engineering

- Introduction to AI Value Creation: value logics, efficiency vs. effectiveness vs. innovation
- Maturity models and organizational prerequisites (data strategy, operating models, AI culture)
- Identification and evaluation of use cases
- Process analysis and AI-supported process design (automation and augmentation)
- Economic analysis of AI projects (KPIs, ROI, risk analysis, business case)
- Application areas in marketing, sales, operations, HR, finance, public administration, and research
- Governance, ethics, and legal frameworks (bias, fairness, data privacy, transparency)
- Implementation of AI in organizations (roles, responsibilities, change management considerations)
- Case studies of successful and failed AI projects

- The software engineering process
- Programming paradigms for applications in the field of AI
- Effective and efficient data structures
- Tools and software ecosystems for the development and testing of software systems
- AI tools to support software development

- Overview of the options available in the design of new interactive systems and the revision of existing ones
- Creative techniques, concepts, and methods for generating ideas that enable both more intuitive operation and more innovative uses of existing and new interactive systems
- Practical implementation of innovations from a management perspective
- Development of a holistic understanding of the subject areas of systemic management
- Methods for generating innovative ideas (e.g., Systematic Inventive Thinking, TRIZ, Design Thinking)
- Project structures and management methods for the practical implementation of innovations (e.g., MELT Frame)

- Data modeling for relational data structures
- Database interaction in SQL (DDL, DML, DQL)
- Non-relational data storage concepts (NoSQL databases)
- Implementation of data structures
- Integration of data structures into applications

- Advanced and in-depth topics in the field of software engineering
- In-depth topics in the subfields of requirements analysis, such as the SOPHIST rule set and goal modeling, and design, such as design patterns and design principles
- Further study of agile development methodologies
- Approaches to the development of extensible software systems
- Automation in the development of large-scale software systems, such as unit testing, continuous integration, and DevOps

- Ways in which software project management can be supported by tools.
- Different areas of project management, such as source code management, bug tracking, testing, and deployment.
- Discussion of tools, such as GitLab for source code management or Jenkins for CI/CD.
- Ways in which individual tools can be integrated with one another.
- Examination of the mechanisms of gender roles, bias, and structural disadvantage, as well as practical approaches to the gender-inclusive design of work and learning environments.
- Introduction to models and methods for analyzing stressors and resources in relation to work-life balance.

- Architectural models for scalable software development and systems
- Integration models and paradigms for implementing complex, process-oriented software ecosystems
- Application of established design patterns
- Design and implementation of efficient and scalable software systems

- Role of software architecture in the software development process
- Design of software architectures
- Architectural patterns and architectural aspects, such as internationalization
- Application of modern software architectures
- Documentation of software architectures using UML

Fundamentals and Modeling:
-Introduction to digital twins, their significance, and areas of application
-Presentation of the theoretical foundations and methods of modeling

Simulation and Software:
-Overview of simulation techniques and their typical applications
-Introduction to various simulation software tools and practical exercises

Smart Products and Solutions:
-Development and analysis of models for smart, connected products
-Integration of digital twins into IoT systems and their benefits

Advanced Concepts and Applications:
-Introduction to advanced topics such as condition monitoring and predictive maintenance
-Discussion of the role of digital twins in future technological development

Practical Project:
-Planning and implementation of an individual digital twin project to apply the knowledge acquired

- Visually oriented analytics tools, e.g., BI tools such as MS Power BI, Tableau, QlikView
- Visualization libraries, e.g., matplotlib.pyplot, ggplot2
- Principles of visual communication, e.g., Hichert SUCCESS

- Foundations of Human–AI Interaction
- Human cognition, mental models, decision-making behavior
- HCI principles for AI systems
- UX Design for AI
- Interaction patterns: conversational UIs, agents, multimodal interfaces
- Prompt design, system messages, feedback loops
- Trust and user acceptance
- Human-in-the-Loop and Human-on-the-Loop
- Alignment of human–machine roles
- Task allocation and the limits of automation
- Error prevention and recovery design
- Empathic and Social Dimensions of AI Interaction
- Social responses and trust
- Persuasive and adaptive systems
- Methods and Tools
- Usability testing for AI systems
- Prototyping tools
- Evaluation methods: task success, trust metrics, cognitive load

- Fundamentals of descriptive statistics
- Measurement system analysis
- Sample size determination
- Statistical process control
- Process control charts
- Process capability analysis
- Components of variance analysis (COV)
- Review of the fundamentals of inferential statistics
- Determination of error causes through hypothesis testing (t-test, chi-square test, ANOVA)
- Multiple regression analysis

The content of this course is not fixed; rather, it is adapted to current prevailing trends. Representative topics may include:

- New technologies in the field of big data processing
- Trends in programming languages for data analysis
- New concepts for data processing (e.g., data lakes)
- New research questions in the field of data science
- New practical issues in the field of data science

The content of this course is not fixed; rather, it is adapted to currently prevailing trends. Exemplary topics may include:

- Current best-practice approaches and concepts in application areas (e.g., smart home, smart city, smart production, connected vehicles, etc.)
- Current best-practice approaches regarding development processes and tools
- Current research and development activities as well as research and development outcomes

The content of this course is not fixed; rather, it is adapted to currently prevailing trends. Representative topics may include:

- New technologies in AI engineering
- Trends in programming languages for AI
- New design concepts in AI
- Emerging research questions in the field of AI
- Emerging issues in the field of AI

- Current developments in the field of enterprise application systems, with particular reference to ERP systems and business process management
- Models, examples, and best-practice cases

- Different models of LLMs
- Advantages and disadvantages of these models
- Use cases for these models
- Different technology stacks for operating such models
- Challenges in planning and operating local models

- Introduction to various application-oriented analytics platforms (e.g., KNIME, RapidMiner, Grafana)
- Introduction to various cloud solutions for data analytics (e.g., Google Cloud, AWS, Azure)
- Application of the presented platforms using sample analytical datasets
- Discussion of the different approaches

- Workforce planning and resource management
- Contract management and financial management (cost and performance accounting)
- Quality management and quality assurance (reviews, audits, etc.)
- Employee information and communication
- IT strategy and IT portfolio management
- Risk management; IT governance and compliance
- ITIL, COBIT
- Service-level agreements and outsourcing
- Balanced Scorecard methodology
- IT management tools (ADOit, etc.)

Introduction
- IoT architecture (e.g., reference models)
- Requirements for IoT systems
- IoT data transmission protocols
- Use of IoT in industrial contexts (examples)
- Fundamentals of sensor technology
- Fundamentals of embedded systems

Implementation
- Approaches to IoT implementation
- Prototypical implementation of IoT
- Selection of sensors
- Collection, visualization, and analysis of data
- Challenges in implementation

- Basic concepts: business process, workflow, BPMS, WFMS, RPA, etc.
- Selection criteria for workflow engines for process automation
- Architecture and integrations of workflows for process automation
- Overview of interprocess communication
- Transactional properties of processes, simulation, and code generation
- Fundamentals of Microsoft Dynamics 365: modules and navigation, basic entities, and standard workflows
- Organizational and technical implementation using configuration and declarative programming

- Strengthening reflective capacity by reflecting on the content of the semester’s courses in an interdisciplinary context in order to further consolidate learning and clarify open questions. This may be accomplished, for example, through advanced exercises or workshop-style formats.
- Advanced development of problem-solving competence through the deepening and integration of subject areas across different courses, which are addressed in an in-depth project and thereby provide students with further insights; where appropriate, this may also serve as preparation for their own scholarly work, for example in preparation for the master’s thesis. In this seminar, particular emphasis should be placed on subject areas related to development and engineering, as well as web- and AI-based systems.
- Linking professional practice-related information with students’ own scholarly work: enabling students to connect professional experiences from their field of activity with the content of the semester’s courses in a more systematic and scholarly form, which, similar to point 2, may serve as preparation for their own scholarly work.

Under the guidance of the course instructor, students gather information about potential study trip destinations, research relevant data and facts about the destination country, and organize the program. The week in an international environment is characterized by company visits, attendance at lectures at partner universities, as well as presentations and events in the area of social skills. The aim is to ensure that students develop an understanding of the principal cultural trends of the respective country. Discussions with professionals and executives, visits to foreign trade centers, and meetings with business and social associations complement the international and personal development experiences of the study trip.

- Strengthening reflective capacity through reflection on the semester’s course content in an interdisciplinary context, with the aim of further consolidation and clarification of open questions. This may be achieved, for example, through advanced exercises or workshop-style formats.
- Advanced development of problem-solving competence through the deepening and integration of thematic areas across various courses, addressed in an in-depth project and thereby providing students with further insights; where applicable, this may also serve as preparation for their own scholarly work, for example in preparation for the master’s thesis. In this seminar, particular emphasis should be placed on topics related to development and engineering, as well as web- and AI-based systems.
- Linking professional-practical information with students’ own scholarly work: enabling students to connect professional experience from their field of activity with the content of the semester’s courses in a more systematic and scholarly form, which, similar to the second point, may serve as preparation for their own scholarly work.

- Advanced knowledge of scholarly research practices
- Development of techniques and rules for scholarly research in data analysis, including inferential statistics
- Practical application of the knowledge outlined above
- Discussion and critical examination of the research methodology of a master’s thesis
- Review and discussion of possible topics and hypotheses

- Implementation of a complex project by students
- Conceptualization, budgeting, and execution, as well as evaluation and interpretation of the results
- Analysis of audience behavior, economically responsible decision-making competence, risk management, intercultural competence, organizational and social competence, budgeting competence, sponsorship, and project management

As part of the master’s thesis colloquium, students receive seminar-based supervision and guidance in preparing their master’s thesis, in addition to individual thesis supervision. The colloquium provides an opportunity to present the research concept and results already developed, and to discuss and critically reflect on them within the group.

The topic of the master’s thesis must be selected from the subject area of the degree program. The research question developed is addressed in the form of a scholarly work; this is to be done independently and without external assistance, with all sources and aids properly indicated. This approach ensures that students are able to address a problem both scientifically and in an application-oriented manner. Students are expected to develop the topic, outline, and timeline independently, particularly through critical engagement with possible research questions and hypotheses. The advisors guide students in this process. Scientific methodology and formal requirements are discussed in individual coaching sessions, as are issues related to time management.

The thesis may be written in either German or English.

- Introduction to knowledge-based AI: forms of representation, ontologies, knowledge graphs
- Challenges of statistical models (hallucination, lack of consistency, context limitations)
- Retrieval-Augmented Generation (RAG): architectures, indexing, embeddings, pipelines
- Knowledge graphs: construction, querying (SPARQL), reasoning, semantic technologies
- Hybrid AI approaches: integrating GenAI, classical AI methods, and symbolic reasoning
- Ontology-guided prompting and constraint-based prompting
- Evaluation methods: knowledge grounding, fact-checking, consistency metrics

Machine Learning Fundamentals
- Introduction to machine learning, learning paradigms, and the machine learning pipeline
- Classical machine learning methods, including regression, tree-based methods, ensemble methods, and clustering
- Fundamentals of neural networks and deep learning
- Model performance, evaluation, bias–variance tradeoff, and cross-validation

Generative AI

- Overview of generative models: large language models, generative adversarial networks, diffusion models, and variational autoencoders
- Transformer architecture and attention mechanisms
- Pretraining, fine-tuning, and instruction tuning
- Fundamentals of prompting and controllability
- Introduction to retrieval-augmented generation
- Image-based and multimodal generative models

- History and overview
- Cloud computing concepts, such as service models and deployment models
- Fundamental technologies of cloud computing, including virtualization and infrastructure management
- Recent developments in cloud computing, such as containerization and serverless computing
- Strategies, tasks, challenges, and risks in cloud sourcing
- AI cloud services
- Sustainability and resource consumption of cloud computing versus on-premises computing

- Foundations of agentic AI: concepts, properties, architectures
- Design of agent systems: modularization, orchestration, role models
- Integration into business and knowledge processes
- Frameworks and tools: e.g., LangGraph, AutoGen, CrewAI, Hugging Face Agents, Assistants APIs
- Multi-agent systems: interactions, delegation, collaboration
- Practical development projects

IT Security Fundamentals
- Security objectives (CIA), threat models, risk assessment
- Authentication, authorization, identity management
- Fundamentals of cryptography (hashing, symmetric/asymmetric methods)

Security in Machine Learning and AI Systems
- Characteristics of modern AI systems
- AI-specific attacks:
- Prompt injection, indirect prompt injection
- Jailbreaking and hallucinations
- Data poisoning and backdooring
- Adversarial examples
- Model extraction and model inversion
- Attack surfaces in LLMs, agentic AI, and RAG systems
- Protective mechanisms: guardrails, red teaming, input validation, sandboxing

Secure GenAI and LLM Integration
- API security and access control
- Data and metadata security
- Logging, monitoring, and audit trails
- Content filtering, safety layers, and moderation

- Data protection (e.g., GDPR)
- Privacy (ePrivacy Regulation)
- Handling of data from an ethical/moral perspective
- Compliance