Modules WS 2024/2025

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

Autonomous design tools are fundamentally changing how designers work across various industries. Autonomous 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.

Autonomous design tools allow for the generation of a variety of diverse design artifacts, including next-generation computer chips, software for specific domains, three-dimensional virtual worlds, and large amounts of content for video games and feature films. The applications for such autonomous 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 autonomous 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 autonomous design tools by

• discussing the conceptual foundations of autonomous design tools.
• discussing how autonomous design tools change the nature of work and the role of human designers.
• analysing examples of using autonomous 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 autonomous 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

Data Visualisation covers techniques for creating effective data visualisations based on principles from statistics, cognitive science, and graphic design to help analysts and decision-makers understand and explore big data. The course covers eight primary topics:

• Visualising univariate and multivariate numerical data
• Visualising time series data
• Visualising geospatial data
• Visualising networked data
• Visualising high-dimensional data
• Visualising textual data
• Interactive dashboards
• Animations

Digital Entrepreneurship covers the intersection between digital technology and new venture creation, i.e., company start-up activity. It addresses venture creation of digital artefacts as the core market offering (e.g., software, hardware, smart devices), digital technology as enablers of new venture creation (e.g., 3D printing, crowdfunding, platforms such as appStore), and venture creation in technology-intensive contexts (e.g., BioTech, IT Healthcare, FinTech). The course covers six primary topics:

• Forms and processes of entrepreneurship
• Business planning for new ventures
• Digital technologies as enablers and triggers for entrepreneurial activity
• Digital technologies as market offerings of emergent ventures
• Start-up activity in technology-intensive sectors

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

Emerging IT Topics addresses recent technological trends and developments in research and business, so its content can be adapted quickly to the job market’s emerging needs. Accordingly, the course content changes from semester to semester.

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).

ML DevOps and Advanced ML Topics contains two parts. The first is about machine learning development operations. It coverskey aspects of how to turn a business problem into a machine learning product. It starts from requirements engineering, onto building prototypes onto deployment and maintenance. The second part covers several advanced topics in the field of machine learning. Students learn about contemporary ML topics and in particular, how to analyse large amounts of data. In more detail, the topics covered are:

• Requirements engineering for machine learning and business intelligence projects
• ML in Production - from models to products that are monitored and updated
• Advanced ML topics, e.g., AutoML, Time series analysis
• Distributed and parallel computing for machine learning and data processing with a focus on Spark

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 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