- Technische Grundlagen der Lichttechnik und Lichtanwendung
- Funktion und Beschreibung der künstlichen Beleuchtung
- Einführung in die Tageslichttechnik
- Konzepte der Lichtplanung
- Uebungen mit Licht im Modell-Raum.
- Prinzipien der Energieeffizienz und der Lichtqualität
- Planung von einfachen Beleuchtungssituationen
a) in Innenräumen: Leseplatz-, Arbeitsplatz-, Maschinen-, Regal- und Vitrinenbeleuchtung
b) Stadtbeleuchtung: Fassade, Plätze, Parks
- Planung der Natur- und Kunstlichtsituation für einen selbst entworfenen Raum.

Vorlesungen mit anschliessendem Uebungsteil.
Besichtigung von Ausstellungsräumen und Labors.

Die Studierenden
- verfügen über ein Lichtbewusstsein.
- erlangen eine Uebersicht in diesem Fachgebiet und sind befähigt, das Licht bewusst anzuwenden.
- verfügen über ein kritisches Verständnis der historischen und der zeitgenössischen Entwicklung der Lichttechnik.
- kennen grundlegende Prinzipien und Methoden der Lichtplanung und sind befähigt, diese Kenntnisse als Architekten verantwortungsbewusst in ihrem Schaffen anzuwenden.
- sind befähigt eine Vielzahl von Vorgaben und Informationen zu verarbeiten, zu bündeln, zu interpretieren sowie innerhalb des Entwurfsprozesses ausführungsrelevant einzusetzen.
- verfügen über ein grundlegendes Verständnis der Zusammenarbeit mit Kollegen, um Entwurfsideen zu entwickeln.
- sind befähigt komplexe Informationen verschiedenen Zielgruppen und mit verschiedenen Zielsetzungen mitzuteilen.
- sind befähigt kritische Analysen sowie Bewertungen und Synthesen von Ideen, Konzepten, Informationen und für die Disziplin der Architektur relevanten Belangen anzustellen.
- sind befähigt bei der Bildung eines Urteils auf verschiedene Quellen zurückzugreifen.

Anwesenheitspflicht: min. 75% verpflichtend

Security Management covers technical and organizational methods for the definition and implementation of security policies. The course covers six primary topics:

• Definition of security policies
• Security measures and their implementation
• Risk analysis in information security
• Security audit
• Standardization of security management
• Security management tools and best practices

• The module involves interactive lectures with exercises to integrate theoretical knowledge with practical design and analysis skills.
• The e-learning platform Moodle is used throughout the course to disseminate course material and for information and discussion.
• Homework assignments are used to deepen students’ understanding of the analytical methods of security management.

After successful completion of the course, students will:

• understand the key concepts of security management
• understand the main security standards
• be able to carry out risk analyses and use it in planning security measures
• be able to perform an elementary security audit

Network and System Security covers advanced security mechanisms in computer networks and systems and attacks against information systems. The course focuses on eight primary topics:

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

• The module involves interactive lectures with exercises to integrate theoretical knowledge with practical design and analysis skills.
• The e-learning platform Moodle is used throughout the course to disseminate course material and for information and discussion.
• Lab exercises and programming assignments are used to support the acquisition of practical skills.

After successful completion of the course, students will:

• understand the typical attacks against various components of information systems
• understand the main network security protocols and their implementation
• understand the main preventive security mechanisms in operating systems

The following conditions need to be met prior to registering for the module:

• successful completion of the module "Data and Application Security"

Process Mining covers conceptual foundations, methods, and technologies for analysing business processes with the help of digital trace data that stems from information technology. In particular, students learn how to mine digital trace data. The course focuses on three primary topics:

• Petri-net foundations of process analysis
• Process mining algorithms
• Process mining tools and applications

• The course involves interactive lectures with exercises to integrate theoretical knowledge with practical design and analysis skills.
• The e-learning platform Moodle is used throughout the course to disseminate course material and for information and discussion.

After successful completion of the course, students will:

• understand the foundational concepts of process mining
• understand how process mining algorithms work
• understand how a process mining project can be conducted in practice

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

• The course involves interactive lectures with exercises to integrate theoretical knowledge with practical design and analysis skills.
• The e-learning platform Moodle is used throughout the course to disseminate course material and for information and discussion.
• Real-life examples are used to show how the course content can be applied in practice.

After successful completion of the course, students will:

• understand the main concepts, theories, and methods of data visualisation
• recognise the typical challenges of visualising large and complex data sets
• be able to create graphs like bar charts, scatterplots, line charts, and heatmaps to represent various types of data sets visually
• be able to use data-visualisation methods to analyse business problems, generate possible solutions, and compare these solutions in terms of their effectiveness and efficiency

• Artificial Intelligence and Deep Learning covers the basics of artificial intelligence and deep learning and recent technological trends. The course covers five primary topics:Fundamentals of artificial intelligenceFundamentals of deep learning, network design, and trainingConvolutional neural networks, illustrated through image recognitionRecurrent neural networks, illustrated through text miningDeep reinforcement learning - Learning to play games and beyond: Google’s AlphaGo

• The course involves interactive lectures with exercises to integrate theoretical knowledge with practical design and analysis skills.The e-learning platform Moodle is used throughout the course to disseminate course material and for information and discussion.

• After successful completion of the course, students will:understand the basic concepts and methods of artificial intelligence and deep learningbe able to identify suitable applications for artificial intelligence and deep learningbe able to select, use, and adjust existing models and methods for a given task or data set

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 research
• Scientific writing
• Ethical standards
• Literature reviews
• Qualitative research
• Quantitative research
• Mixed-methods research
• Design science research
• Theories used in Information Systems research

• The course involves interactive lectures with exercises to integrate theoretical knowledge, practical design, and analysis skills.
• The e-learning platform Moodle will be used throughout the course for the dissemination of course material and for information and discussion.

After successful completion of the course, students will:

• understand the historical development and concept of scientific research.
• understand the fundamentals of scientific writing.
• be familiar with the most common issues related to research ethics, including plagiarism.
• will know the Association for Information Systems (AIS) Code of Research Conduct.
• be able to identify appropriate theories to explain empirical phenomena.
• be able to identify suitable research methods in order to seek answers to specific research questions.
• be able to use appropriate qualitative, quantitative, mixed-methods and design-oriented approaches to scientific research.

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.

• The course involves interactive seminars that integrate theoretical knowledge with analysis skills.
• The e-learning platform Moodle is used throughout the course to disseminate course materials and for information and discussion.
• Contemporary scientific publications from the fields of Information Systems and Management are discussed in class.

After successful completion of the course, students will:

• have profound knowledge of current topics in IT
• be able to assess the usefulness and potential applications of emerging IT applications
• be current on recent scientific results on emerging IT topics

Human-Centred Design is an approach that places people at the core of every decision point throughout the design process. Identifying, understanding and fulfilling people’s needs, desires, wishes, and goals are imperative in human-centred design. The approach is relevant to any design endeavour that aims to deliver useful products, services, and combinations of both to people as the end-users. The same applies to the design of software, mobile applications, collaboration platforms, and other information systems.

This course is designed with Information Systems students’ needs and goals in mind. Students are guided through their journey in understanding the basics of human cognition and human behaviour that are relevant to the design of information systems. They also learn several methods of human-centred design that are applicable in their projects.

• The module involves interactive lectures with exercises to integrate theoretical knowledge with critical analysis skills.
• The e-learning platform Moodle is used throughout the course to disseminate course material and for information and discussion.
• Case studies are used to discuss the course contents.
• Contemporary scientific publications from Information Systems and Human-Centred Design are discussed in class.

After successful completion of the course, students will:

• understand the basics of human cognition and human behaviour that are relevant to the design of information systems
• understand different human-centred design methods
• be able to apply the understanding and the design methods into their own design projects or illustrative cases

The course Intrusion Detection and Mitigation covers the essential techniques for detection and mitigation of attacks against information systems. The course covers twelve primary topics:

• Taxonomy of Intrusion-detection methods
• Implementation of intrusion detection systems
• Malware functionality and operation
• Static and dynamic malware analysis
• Malware detection and classification
• Security incident response

• The module involves interactive lectures with exercises to integrate theoretical knowledge with practical design and analysis skills.
• The e-learning platform Moodle is used throughout the course to disseminate course material and for information and discussion.
• Homework and programming assignments are used to support the acquisition of practical skills.

After successful completion of the course, students will:

• understand the key techniques deployed in complex attacks against information systems
• understand the methods used in detection of various kinds of attacks
• know the main types of modern malware
• understand the methods used for malware analysis
• be able to apply detection methods on exemplary attack data
• be able to analyse exemplary malware data