AISA Certificates: Overview
Photo: Nadine Koch, Felix Fritzen
Our offerings
How to select what is right for you
Below, we give information on
- our Course Catalog and how to select from it
- the AISA Study Project
- the course Critically Reflecting in Intelligent Systems in Society (CRISS)
- and the Interdisciplinary AI Software Project.
Please check our overview graphics on this page, too.
Process for the AISA certificates (enrollment to finalization)
Image: Nadine Koch, Felix Fritzen
Curriculum for MICRO, CAS and DAS
AISA Study Project (3 ECTS, MICRO & CAS & DAS)
The AISA Study Project takes place in the form of an AISA Hackathon. In the hackathon, participants will embark on an end-to-end journey of building a machine learning pipeline for a gaming application. Teams will start by collecting and preprocessing relevant in-game data, then use this dataset to train machine learning models tailored to enhance the gaming experience. Participants will perform inference, applying their models to real-time data. Finally, teams will present their findings, showcasing how their models improve gameplay dynamics, user engagement, or system efficiency. This event will provide hands-on experience with the full machine learning lifecycle, fostering creativity, collaboration, and technical skill development. The AISA Study Project is vital to all three certificates (MICRO, CAS, and DAS).
Course selection
Depending on the chosen type of certificate, you need to select the following number of courses:
- 1 course for a Microcredential (MICRO)
- 2 courses for a Certificate of Advanced Studies (CAS), out of which at least 1 compulsory course
- 4 courses for a Diploma of Advanced Studies (DAS), out of which at least 2 compulsory courses
- for CAS and DAS the course Critically Reflecting on Intelligent Systems in Society is obligatory (see below for details)
- for DAS the Interdisciplinary AI Software Project is obligatory (see below for details)
In the below course catalog, compulsory courses are labeled via COMPULSORY (use find-as-you-type to filter for them). Note that for the Microcredential (MICRO) any course (compulsory or not) can be chosen from our catalog.
Critically Reflecting on Intelligent Systems in Society (3 ECTS, compulsory for CAS & DAS)
In this course, students will learn how to critically reflect on the use of intelligent systems in society. This includes assessing the potential effects and formulating well-founded positions on ethical questions. Critical reflection is relevant for any application whether it is in engineering, simulations, aviation, education, data science, or any other area. The course will enable students to transfer their knowledge and implement it in their work. Moreover, they will be able to discuss issues and questions with others who might not think the way they do and open their mind to different perspectives.
Interdisciplinary AI Software Project (6 ECTS, compulsory for DAS)
The completion of the Interdisciplinary AI Software Project (6 ECTS) is mandatory for the Diploma of Advanced Studies (DAS) to round up the education in our biggest certificate. It can be completed either during the summer or winter term.
Module Description of the Interdisciplinary AI Software Project in C@mpus
How to use the course catalog?
In the following, we provide our course offerings.
- We provide module names and ECTS points. A short description of each course is given. More information can be found in C@mpus, including details on the lecture, prerequisites, and so forth.
- ST and WT denote summer term and winter term, respectively.
- DE (German) and EN (English) represent the language in which the lecture is given.
- The tag COMPULSORY highlights courses from which you have to choose a certain number for CAS and DAS (and which can also be chosen for MICRO)
Please refer to our FAQ before contacting us.
Work in progress...
...the course catalog is currently being reviewed and extended. Please check for updates regularly (as long as this notes remains).
AISA Course Catalog
- Modeling of Software-Intensive Systems (6 ECTS)
- This course introduces the foundational principles of modeling software-intensive systems, focusing on object-oriented modeling techniques. Key topics include structural and behavioral modeling using UML, formal software architecture modeling, and the modeling of digital twins. Participants will develop the skills to create precise and scalable models applicable to modern software and system architectures.
- Advanced Computing in Architecture
- The module builds on computer science concepts introduced in “Computing in Architecture” to focus on more advanced, artificial intelligence (AI) methods that are relevant for architecture and related fields of engineering. It combines theoretical lectures on, for example,the foundations of generative deep learning and deep reinforcement learning with hands-on tutorials. The module enables students to understand AI methods and to apply them to their own design problems.
- Advanced Software Engineering (6 ECTS, DE, WT)
- This course builds on the introduction to software engineering and provides in-depth knowledge of key areas in software development. Topics include the organizational aspects of software engineering, various software development processes, and their evaluation and improvement. Additional focus areas are requirements analysis, software architecture, implementation and debugging, software quality assurance, and software maintenance. The course also introduces model-driven software development and discusses selected advanced topics in software engineering.
- AI Prototyping 101: From Idea to Reality (6 ECTS for AISA, 3 ECTS as FüSQ, ST, EN)
- The course "AI Prototyping 101: From Idea to Reality" introduces various creativity methods that empower students to tackle everyday challenges and even broader societal issues. Throughout the course, students will develop a prototype concept by applying these creativity techniques and using Artificial Intelligence (AI) tools to bring their ideas to life. In addition, students will learn effective pitching and presentation strategies to showcase their prototypes, gaining hands-on experience in communicating innovative ideas. The course introduces programming concepts and delves into AI's “black box” nature, offering insights into its underlying mechanisms. The course emphasizes teamwork, with students collaborating in groups composed of individuals from diverse disciplines. By the end of the course, participants will have the skills to conceptualize and create a prototype and the confidence to present and pitch their ideas. Requirements: no knowledge of programming or AI theory is necessary
- Basic Principles of Artificial Intelligence (6 ECTS, WT, DE, COMPULSORY)
- The course introduces the basics of artificial intelligence, starting from concepts of intelligence and agents, introducing different kinds of problems and games, and covering different methods and algorithms such as logic-based agents and probabilistic reasoning. The course enables students to classify AI problems and work on them using the methods and algorithms learned.
- Computing in Architecture (6 ECTS, EN, ST)
- This course introduces advanced computational methods like optimization and machine learning in the context of architectural design. Students will learn how to automate the search for good design candidates, how to analyze the resulting data, and how to make predictions from that data. The module focuses on performance-informed architectural design with building simulations, but students will be free to explore other applications of these methods as well.
- Data Processing for Engineers and Scientists (6 ECTS, EN, ST & WT)
- The course teaches basic knowledge of data acquisition, data preparation, data analysis, and data visualization, including elementary knowledge in image processing. Additionally, data-based/-assisted modeling is addressed. An extensive computer lab in Python accompanies the course. Additional material is available (templates, mini tutorials, learning module). The regular course takes place in the winter term. At the end of the summer term, an equivalent intensive course (2 weeks) is offered, as well.
- Einführung in Software Engineering (6 ECTS, ST, DE, COMPULSORY)
- The lecture deals with technical and other aspects of software development as it takes place in practice. The individual topics are: Delimitation and motivation of software engineering, process models, agile approach, Scrum, software management, software testing and quality assurance, methods, languages, and tools for the individual activities: specification, rough design, detailed design, implementation, and testing.
- Foundations of Machine Learning (6 ECTS, ST, EN, COMPULSORY)
- This course provides students with an understanding the mathematical and algorithmic concepts underlying machine learning. They acquire the expertise to formulate and solve machine-learning problems and evaluate the solution.
- Lab Course Artificial Intelligence: Knowledge Representation for Buildings (6 ECTS, DE, ST)
- The design, construction, and operation of a building involve multiple disciplines and processes that must be coordinated throughout its lifecycle. By assigning semantics to digital objects, knowledge can be integrated across these disciplines, ensuring proper alignment and interoperability. In this course, students will develop tools using Knowledge Graphs, Semantic Web technologies, and Machine Learning to facilitate collaborative design. While the primary focus is on the building industry, these techniques are broadly applicable to any field requiring data integration and interdisciplinary collaboration. The course is open to bachelor’s and master’s students interested in Semantic Digital Twins, Semantic Web technologies, and collaborative design in the built environment. Students will work in groups to develop components of a collaborative design application for the building industry. The course includes weekly sessions for teamwork, progress presentations, and cross-group integration. Final deliverables will consist of a demo, a poster session, and a four-page report.
- Machine Perception and Learning (6 ECTS, EN, WT)
- Students will learn about fundamental aspects of modern machine learning methods for perception. Students will learn to implement, train, and evaluate their own neural networks and gain a detailed understanding of cutting-edge research in learning-based computer vision and HCI. Topics covered in the course include convolutional and recurrent neural networks, Transformers, multimodal and interactive machine learning, generative models, explainable and neuro-symbolic AI, (deep) reinforcement learning, and graph neural networks. Theory-focused lectures will be complemented with interactive tutorials in which students will gain hands-on experience with neural network fundamentals, as well as with reimplementation tasks in which students will learn to understand, reimplement, and reproduce selected research papers published at top computer vision and machine learning venues. A course project spanning the second half of the course will involve developing, implementing, and training a complex neural network architecture and applying it on a real-world dataset. Projects will be proposed by members of the Perceptual User Interfaces group and closely linked to their ongoing research.
- Mathematics of Machine Learning
- Coming soon...
- Probabilistic Machine Learning (6 ECTS, EN, WT)
- The course introduces the basics of probabilistic machine learning (including methods of explainable AI, XAI). It covers algorithms and methods such as Bayes’ net, Variable elimination, Lime, SHAP Values, and counterfactuals. The course enables students to classify probabilistic machine learning and address problems using the methods and algorithms learned.
- Quantitative Evaluation of Software Designs (6 ECTS, EN, ST)
- This course teaches key methods of quantitative evaluation of software designs. Key topics include the quantitative analysis using UML2, an overview of analysis models including their statistical foundations and solvers, as well as methods for model checking. Participants will develop the skills to design and evaluate software according to quantitatively analyzable requirements like performance, reliability, safety, scalability, elasticity, costs, and other software qualities.
- Reinforcement Learning (6 ECTS, ST, EN, COMPULSORY)
- Reinforcement Learning considers how an agent, interacting with a world, can improve or learn optimal behavior based on their own experience or teacher demonstration. This branch of Artificial Intelligence and Machine Learning has become an increasingly important foundation of robust intelligent systems and robotics. This course will introduce the theory of Reinforcement Learning and then discuss state-of-the-art algorithms in this area. It will enable students to apply Reinforcement Learning algorithms in simulated domains and real robotic systems.
- Security and Privacy (6 ECTS, DE/EN, ST)
- Participants will gain an in-depth understanding of key topics in information security and privacy, focusing on advanced issues in the field. The topics covered may vary depending on current developments and the focus of the course. Potential topics include Secure Multi-Party Computation, Zero-Knowledge Protocols, cryptographic protocol verification, blockchains and smart contracts, differential privacy, and privacy-preserving data mining, as well as secure e-voting systems. Each of these areas addresses real-world challenges in maintaining security and privacy in modern digital environments.
- Simulation Software Engineering (6 ECTS, EN, WT)
- The course introduces the key concepts of research software engineering tools and their handling. The tools include continuous integration, virtualization, building & packaging, and version control. Moreover, the course provides an overview of important large-scale open-source simulation software packages. Ultimately, the participants learn how to contribute to such software packages and that knowing how to program is not enough to develop sustainable and reusable research software.
- Software Architecture (6 ECTS, EN, WT)
- This course provides a comprehensive introduction to software architectures, their relevance, and fundamental concepts. Participants will explore key architectural patterns and styles, reference architectures, and the relationship between requirements engineering and software architectures. Additional topics include the specification of software architectures, components and component frameworks, architectures for dynamic and self-adaptive systems, and the concepts of services and microservices. The course also covers variability and product-line architectures, architecture evaluation, quality assurance, and optimization. Finally, the role of the software architect and the integration of software architecture into the development process will be discussed.
- System and Web Security (6 ECTS, DE/EN, ST)
- Participants are made aware of common security vulnerabilities and attack vectors in computer systems and the web. They become familiar with specific attacks and the underlying principles, as well as common defense mechanisms. IT systems are continuously under attack from various types of adversaries, including criminal organizations, intelligence agencies, and industrial espionage by states and companies. This course covers the most prevalent attack vectors on computer systems, including mobile devices and the web. Topics include stack and heap overflows, format string vulnerabilities, integer overflows, return-oriented programming, Cross-Site Scripting (XSS), SQL Injections, and Cross-Site Request Forgery (XSRF). Additionally, the course discusses common defense mechanisms, such as access control, address space layout randomization (ASLR), static code analysis, security monitoring, input/output sanitization, and prepared statements.