Vorlesungsverzeichnis Master Wirtschaftsinformatik

AI Agents and Human AI Co-Work touches on two interrelated topics: AI agents and their interaction with humans. It builds upon a basic understanding of Agentic AI and deepens it by elaborating on agent specific challenges such as continual learning, multi-agent collaboration, long running autonomous tasks, and tool usage. It also looks more holistically on how to manage a fleet of agents taking into account technical and human realities. This includes how to design human-AI interaction and workflows with AI given the changing roles of humans in the workforce due to AI agents. Roles of humans might increasingly shift towards managers, potentially defining their own agents or at least providing high level instructions and goals, monitoring agent execution, and reviewing outcomes. This requires mutual understanding. AI agents might take personalisation to the next level, as AI agents are increasingly integrated into our lives.
It also discusses research frontiers.

• Understanding and controlling AI: AI and human capabilities, explainability, interventions with causal AI, guardrails
• Structuring of workflows and jobs: Task decomposition, Modes of interaction (audio, text, visual), collaboration of humans and AI, humans as AI agent managers
• Agent system design: Tool usage, multi-agent systems, continual learning, memory, computational efficiency
• Evaluation of agents and human-AI workflows
• Governance, Safety, and Alignment

AI techniques play an increasingly important role for modern security mechanisms. They are crucial for timely detection of attacks, discovery and analysis of security vulnerabilities, analysis of malicious software. On the other hand, AI methods themselves can be victims of data manipulation and poisoning, or be used by attackers for malicious purposes. AI and Security provides on overview of selected applications of AI techniques in security and reveals the fundamental mechanisms required for assessment of security of AI. Specific topics covered by the course include but are not limited to the following:

• AI methods for intrusion detection
• Malware analysis by means of AI methods
• AI methods for network security
• Data manipulation attacks against AI methods
• Security of AI in practical context
• Offensive AI

AI Solution Specification, Design, and Operations provides deeper insights into key phases of AI (and data science) projects. It deepens foundations on data exploration and visualization, which are key to identify business opportunities and assess data. It also discusses on how to turn ideas into specifications and products. It also elaborates on computational aspects often relevant during operations but turning out to be useful also during early project phases. In summary topics are:

• Identifying and assessing opportunities data: Data exploration and visualization
• Requirements engineering for AI projects
• ML in Production - from models to products that are monitored and updated
• Efficient solutions: Distributed and parallel computing for machine learning and data processing

AI-based design tools are fundamentally changing how designers work across various industries. AI-based design tools make independent design decisions and, in some cases, execute entire design processes. They employ technologies typically associated with artificial intelligence, including machine learning, pattern recognition, meta-heuristics, and evolutionary algorithms.
AI-based design tools enable the generation of a diverse range of design artifacts, including next-generation computer chips, specialized software for specific domains, three-dimensional virtual worlds, and extensive content, such as for video games and feature films. The applications for such AI-based design tools are also expanding to other industries, such as mechanical engineering, aerospace, and architecture.

Instead of creating artifacts by directly manipulating their representations, designers select tools, decide on design parameters, set values for these parameters, and evaluate and learn from the analysis of the results the tools produce. Design work in such situations involves intense interaction with AI-based tools. Designers need to be mindful of the logic, capabilities, and limitations of the tools and the algorithms these tools employ and find ways to make sense of and deal with the often unanticipated outputs of such tools.

The course addresses this increasingly important role of AI-based design tools by

• discussing the conceptual foundations of AI-based design tools.
• discussing how AI-based design tools change the nature of work and the role of human designers.
• analysing examples of using AI-based tools in design practice.
• providing hands-on experience in agent-based modelling for students to simulate the behavior of these tools.
• providing hands-on experience in using AI-based design tools for the design of virtual worlds.

Business Process Analysis focuses on process analysis, covering approaches and methods for designing, analysing, and simulating processes in organisations. The course covers four primary topics:

• Introduction to process analysis
• Process modelling and design
• Process flow analysis
• Process simulation

Business Process Management provides an introduction to fundamental concepts, frameworks, models, theories, and methods in process management and covers the operation, improvement, and innovation of business processes. Business Process Management (BPM) is one of the core topics of the degree programme, so the course also provides a basis on which students can choose their electives. The course covers eight primary topics:

• Business process operations
• Business process change
• Strategic alignment
• Business process governance
• Quality management
• Six Sigma
• BPM skills
• Organizational culture

Business Statistics covers statistical methods that are used to support decision-making in business contexts, so it also provides a methodological foundation for the students’ master’s thesis projects. The course builds on the basic concepts of statistical testing and estimation theory that are usually taught in bachelor’s programmes. The course covers five primary topics:

• Graphic and numeric characterisations of random variables and their distributions
• Framework and basic applications for testing hypotheses and estimating parameters
• The ordinary least squares (OLS) method
• Simple linear regression, including parameter estimation, diagnostic plots, hypothesis testing, predictions, and model specifications using log-transformations

Crypto Finance covers an introduction to the blockchain and its applications to crypto markets and portfolio man-agement. The course builds on the basic concepts of the blockchain technology and Bitcoin. The course then ex-tends to the development of Altcoins and their features. The course concludes with the integration of crypto as-sets into portfolio management.
Key topics covered are:
· Blockchain Technologies
· Consensus Mechanisms
· Forks
· Bitcoin
· Altcoins
· Token Sales
· Crypto Portfolio Management

• Data Management covers the modern data-management cycle, from the collection of data from diverse sources to the preparation of data for data-driven applications. Students learn how to handle various data formats, how to assess and improve data quality, and how to store and process data using SQL, NoSQL, and Hadoop technologies. The course covers eight primary topics:Modern data-management requirementsDatabase system architectureDiagnosing and handling data quality problemsRelational databases (SQL)Hands-on labs with MySQLConcurrency control techniquesNoSQL databases (e.g., MongoDB)Apache Hadoop (HDFS, MapReduce)

Digital Innovation covers the fundamentals of digital innovation and the development and implementation of novel and original solutions in which the innovation process, its outcomes, or the ensuing organisational and social transformation is embodied in or enabled by digital technologies. Digital Innovation is one of the core topics of the degree programme, so the course also provides a basis on which students can choose their electives. The course covers six primary topics:

• Fundamental properties of digital technologies and digital innovation
• Organising for digital innovation
• Digital platforms and ecosystems
• Digital innovation and capital creation
• Digital business models
• Digital entrepreneurship

Extracurriculare Activities comprise of various activities that are not linked to the Curriculum of the MSc in Information Systems, which are optional and further support the studying of the Master programme.

The course is an introduction to the field of Finance, reiterating the most important concepts from a bachelor's degree with a focus on Finance. It builds on the time value of money principle and applies it to the valuation of bonds, interest rates, and capital budgeting. The course also highlights some of the most significant markets for financial instruments. The main goal is to establish a strong foundation for understanding the key concepts of Finance.
Key topics covered are:

• Introduction to financial markets
• Interest rates and bond prices
• Structure of interest rates
• Market efficiency
• Funds markets
• Money markets
• Bond markets

Global Perspectives on Information Technology and Digital Entrepreneurship offers a comprehensive exploration of the global landscape of information technology and digital entrepreneurship. It examines how regional ecosystems, regula-tory environments, and strategic and political decisions influence where and how digital innovation emerges. Through historical analysis, current case studies, expert guest speakers, and applied frameworks, students will develop a global mindset necessary to navigate and build digital ventures across borders.

Focusing on major hubs in Europe, the USA, the Middle East, and Asia, as well as emerging ecosystems (e.g., Liechten-stein, Dubai, Saudi Arabia, Nairobi, Tallinn), the course provides insight into the interplay between talent availability, investment flows, infrastructure, education, and policy. The course is designed to guide students toward understanding the factors that make specific regions thrive and help them reflect on their own potential paths in digital entrepreneur-ship.

• Information Systems Development provides an introduction to programming including web frameworks that can be used in online environments such as e-commerce platforms or blog systems. The course covers three primary topics and is based on Python 3:Introduction to scripting / programmingSoftware / program developmentUsing IDE for software development

The course treats Generative AI (GenAI) and Agentic AI in a holistic manner. It discusses technical aspects relevant to application and implementation such as RAG, tool integration for agents. It also discusses risks and opportunities including a number of use-cases:

• GenAI and Agentic AI Project Lifecycle: From idea to evaluation and monitoring
• Adapting and enhancing foundation models, e.g., using retrieval, tools, fine-tuning and prompting
• Opportunities and risks of AI agents and GenAI
• Use-cases in various industries and for various tasks for AI Agents and GenAI
• Evaluation of GenAI & Agents

• Management Information Systems focuses on large-scale application software packages that support end-to-end processes, information and document flow, reporting, and data analytics in organizational settings. The course covers eight primary topics:Enterprise applicationsE-business Managing knowledge Enhancing decision-making Building management information systems (MIS) Managing projects and global systems MIS-related integration, transformation, innovation, and change Case studies on current MIS topics

In their Master’s Thesis, students use scientific methods to answer a research question and work in accordance with standards of scientific writing. The master’s thesis is typically related to one of the four subject areas that constitute the core of the curriculum (i.e., Business Process Management, Data and Application Security, Data Science, and Digital Innovation).

Network and System Security covers advanced security mechanisms in computer networks and systems and attacks against information systems. It provides a technical overview of selected security mechanisms at the networking and OS level. The course focuses on following topics topics:

• Essential network-security protocols
• Attacks against common network protocols
• Security issues in web applications
• Security mechanisms in operating systems
• Advanced exploitation techniques

The course provides an overview of the relevant aspects of the C/C++ programming language and its respective libraries.

Process Mining provides a comprehensive exploration of the fundamentals of process mining, including conceptual foundations, methods, and technologies used for analyzing business processes with the help of digital trace data recorded in event logs.
Students attending this course will gain knowledge of foundational concepts and algorithms in process mining and acquire practical skills to mine digital trace data using process mining techniques and software. Students will also learn the main steps of conducting a process mining project within an organization as well as common challenges and strategies of process mining analysis.

The course covers four primary topics:

• Petri-net foundations of process analysis
• Process mining algorithms, including process discovery and conformance-checking algorithms
• Process mining project methodologies, strategies, and challenges of process mining analysis
• Process mining tools and applications

In Project Seminar, students analyse a real-world case from a specific industry. Students divide into groups according to their preferences and work on one of four cases through the lens of process management, data and application security, data science, or digital innovation. The course topics change from semester to semester.

In Project Seminar, students analyse a real-world case from a specific industry. Students divide into groups according to their preferences and work on one of four cases through the lens of process management, data and application security, data science, or digital innovation. The course topics change from semester to semester.

Projects typically aim to turn data into value and leverage technological advancements. They might focus on business aspects, such as identifying use-cases for novel AI models. Projects often include hands-on work on the data side, such as data mining and collection, as well as on the model side, such as leveraging and adjusting AI models, including foundation models. Projects are expected to deliver conceptual designs or conduct practical case studies on various aspects of data science and artificial intelligence.

In Project Seminar, students analyse a real-world case from a specific industry. Students divide into groups according to their preferences and work on one of four cases through the lens of process management, data and application security, data science, or digital innovation. The course topics change from semester to semester.

Projects will address various aspects of business process management and digital transformation. Exemplary topics include but are not limited to process elicitation and understanding, analysis of process related data, digital business models, change management, human-system interaction. Projects are expected to deliver conceptual designs or to carry out practical case studies on various aspects of digital business.

In Project Seminar, students analyse a real-world case from a specific industry. Students divide into groups according to their preferences and work on one of four cases through the lens of process management, data and application security, data science, or digital innovation. The course topics change from semester to semester.

Projects will address security applications in practical context. Exemplary topics include but are not limited to security awareness, security management, data protection, incident response, blockchain applications, penetration testing. Projects are expected to deliver either conceptual designs for security operations or specific tools and their assessment.

In Project Seminar, students analyse a real-world case from a specific industry. Students divide into groups according to their preferences and work on one of four cases through the lens of process management, data and application security, data science, or digital innovation. The course topics change from semester to semester.

Projects will address various aspects of digital innovation, focusing on developing and implementing novel solutions. Exemplary topics include but are not limited to digital transformation strategies, AI management, innovation ecosystems, digital product design, and the impact on organizational and social transformation. Projects are expected to deliver conceptual designs or practical case studies demonstrating how digital technologies enable or embody the innovation process and its outcomes.

• In Research Methods, students learn to identify pertinent research questions, conduct systematic literature reviews, apply appropriate research methods, and report on their results. The course covers nine primary topics:Introduction to scientific researchScientific writingEthical standardsLiterature reviewsQualitative researchQuantitative researchMixed-methods researchDesign science researchTheories used in Information Systems research

In the Research Seminar course, students learn to apply in practice what they learned in the Research Methods course. The seminar covers issues related to identifying and formulating research questions, choosing a suitable research design to use in answering these questions, evaluating the feasibility of a planned research study, and writing research proposals. Together with faculty, students develop research proposals (so-called “exposés”) for their master’s theses.

Web-based Information Systems covers subjects related to Web-related technologies. The goal is providing a solid understanding of the “Internet”, which is a cornerstone of modern Information Systems. Students are expected to learn a solid basis that can be further expanded by other courses taught in the MSc. in Information Systems. The main topics discussed in Web-based Information Systems entail:

· Basic architectures of Distributed Systems
· Routing and Switching
· IP and MAC addresses
· Domain Name System
· Client/Server
· Content Delivery Network
· Local Area Network and Wide Area Network
· Virtual Private Network
· Transmission Control Protocol and User Datagram Protocol