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Teaching Overview

Institute for Artificial Intelligence (KI)

Check bellow our course offerings over the semester.

In the following, you can find the teaching activities organized by each Department of the Insitute of Artificial Intelligence.

Please always use the links available on C@mpus if you are interested in enrolling.

Analytic Computing

Oberseminar Artificial Intelligence

Colloquium

Number:	021898001
Lecturers:	Univ.-Prof. Dr. rer. nat. Steffen Staab
Type:	Colloquium
Semester hours:	2
Teaching Language:	German/English
Semester:	SS 24
Organisation:	Analytic Computing
Content:	nur für Mitarbeiterinnen und Doktorandinnen
Link:	C@MPUS

Machine Learning (Exercise for Engineering Students)

Exercise

Number:	021832002
Lecturers:	Univ.-Prof. Dr. rer. nat. Steffen Staab Osama Mohammed
Type:	Exercise
Semester hours:	2
Teaching Language:	English
Semester:	SS 24
Organisation:	Analytic Computing
Content:	siehe Vorlesung für alle Studierenden, die NICHT in der Fakultät 5 oder in SimTech studieren
Link:	C@MPUS

Machine Learning (Exercise for Engineering Scientists)

Exercise

Number:	021832003
Lecturers:	Univ.-Prof. Dr. rer. nat. Steffen Staab Tim Schneider
Type:	Exercise
Semester hours:	2
Teaching Language:	English
Semester:	SS 24
Organisation:	Analytic Computing
Content:	siehe Vorlesung für Promotionsstudierende
Link:	C@MPUS

Machine Learning (Exercise for Data Science students)

Exercise

Number:	021832001
Lecturers:	Univ.-Prof. Dr. rer. nat. Steffen Staab Tim Schneider Yi Wang Farane Jalali Farahani Osama Mohammed Daniel Frank Akram Sadat Hosseini
Type:	Exercise
Semester hours:	2
Teaching Language:	English
Semester:	SS 24
Organisation:	Analytic Computing
Content:	siehe Vorlesung nur für Studierende des B.Sc. Data Science
Link:	C@MPUS

Machine Learning

Exercise

Number:	021832000
Lecturers:	Univ.-Prof. Dr. rer. nat. Steffen Staab Tim Schneider Yi Wang Farane Jalali Farahani Osama Mohammed Daniel Frank Akram Sadat Hosseini
Type:	Exercise
Semester hours:	2
Teaching Language:	English
Semester:	SS 24
Organisation:	Analytic Computing
Content:	Siehe Vorlesung Für Studierende der Studiengänge des gesamten Fachbereichs Informatik
Link:	C@MPUS

Machine Learning

Lecture

Number:	021831000
Lecturers:	Univ.-Prof. Dr. rer. nat. Steffen Staab Tim Schneider Yi Wang Farane Jalali Farahani Osama Mohammed Daniel Frank Akram Sadat Hosseini
Type:	Lecture
Semester hours:	2
Teaching Language:	English
Semester:	SS 24
Organisation:	Analytic Computing
Content:	Exploiting large-scale data is a central challenge of our time. Machine Learning is the core discipline to address this challenge, aiming to extract useful models and structure from data. Studying Machine Learning is motivated in multiple ways: 1) as the basis of commercial data mining (Google, Amazon, Picasa, etc), 2) a core methodological tool for data analysis in all sciences (vision, linguistics, software engineering, but also biology, physics, neuroscience, etc) and finally, 3) as a core foundation of autonomous intelligent systems (which is my personal motivation for research in Machine Learning). This lecture introduces to modern methods in Machine Learning, including discriminative as well as probabilistic generative models. A preliminary outline of topics is: motivation regression: linear regression, kernel methods classification: kNN, Naive Bayes, logistic regression, decision trees, support vector machines ensemble methods: bagging and boosting neural networks: mixture distributions, backpropagation, CNNs, RNNs clustering: K-Means, EM, agglomerative clustering, PLSA dimensionality reduction Cross-cutting topics: evaluation, loss functions, regularization, gradient descent
Link:	C@MPUS

Lab Course Artificial Intelligence: Deep Learning Lab

Exercise

Number:	023887000
Lecturers:	Univ.-Prof. Dr. rer. nat. Steffen Staab Bo Xiong Mojtaba Nayyeri
Type:	Exercise
Semester hours:	4
Teaching Language:	English
Semester:	SS 24
Organisation:	Analytic Computing
Content:	https://www.f05.uni-stuttgart.de/informatik/dokumente/Seminare/Fachpraktika_23S/07_LC-ArtificialIntelligence.pdf
Link:	C@MPUS

Knowledge Graphs

Exercise

Number:	022500205
Lecturers:	Univ.-Prof. Dr. rer. nat. Steffen Staab Dr. rer. nat. Daniel Hernandez Cosimo Gregucci
Type:	Exercise
Semester hours:	2
Teaching Language:	English
Semester:	SS 24
Organisation:	Analytic Computing
Content:	Siehe Vorlesung
Link:	C@MPUS

Knowledge Graphs

Lecture

Number:	022500200
Lecturers:	Univ.-Prof. Dr. rer. nat. Steffen Staab Dr. rer. nat. Daniel Hernandez Cosimo Gregucci
Type:	Lecture
Semester hours:	2
Teaching Language:	English
Semester:	SS 24
Organisation:	Analytic Computing
Content:	• motivation: application cases and examples for knowledge graphs• graph data models and the languages that can be used to query them • representations of schema, identity, and context in knowledge graphs• deductive reasoning in knowledge graphs• inductive reasoning in/for knowledge graphs• creation and enrichment of knowledge graphs• principles and protocols for managing and publishing knowledge graphs
Link:	C@MPUS

Intelligent Sensing and Perception

Advanced seminar on Foundations of Mobile Robotics

Advanced Seminar
2 Sh
SS 24

Number:	024000200
Lecturers:	Univ.-Prof. Dr. Kai Arras Till Hielscher Dennis Rotondi
Type:	Advanced Seminar
Semester hours:	2
Teaching Language:	English/German
Semester:	SS 24
Organisation:	Autonome Systeme
Content:	https://www.f05.uni-stuttgart.de/informatik/studierende/seminaranmeldung/
Link:	C@MPUS

Machine Learning for Simulation Science

Theoretical and Methodological Foundations of Autonomous Systems

Exercise
2 Sh
WS 23/24

Number:	021822000
Lecturers:	Univ.-Prof. Ph.D. Mathias Niepert
Type:	Exercise
Semester hours:	2
Teaching Language:	English
Semester:	WS 23/24
Organisation:	Maschinelles Lernen in den Simulationswissenschaften
Content:	for M.Sc. Computer Science students: You have to attend the course "foundations of artificial intelligence" (LV-no. 020411000) https://campus.uni-stuttgart.de/cusonline/pl/ui/$ctx/wbLv.wbShowLVDetail?pStpSpNr=302564&pSpracheNr=1 If you registere here, we will transfer your registration to the above mentioned course as this is actually the course this module refers to.
Link:	C@MPUS

Theoretical and Methodological Foundations of Autonomous Systems

Lecture
2 Sh
WS 23/24

Number:	021821000
Lecturers:	Univ.-Prof. Ph.D. Mathias Niepert
Type:	Lecture
Semester hours:	2
Teaching Language:	English
Semester:	WS 23/24
Organisation:	Maschinelles Lernen in den Simulationswissenschaften
Content:	for M.Sc. Computer Science students: You have to attend the course "foundations of artificial intelligence" (LV-no. 020411000) https://campus.uni-stuttgart.de/cusonline/pl/ui/$ctx/wbLv.wbShowLVDetail?pStpSpNr=302564&pSpracheNr=1 If you registere here, we will transfer your registration to the above mentioned course as this is actually the course this module refers to.
Link:	C@MPUS

Reinforcement Learning

Exercise
2 Sh
SS 24

Number:	021862000
Lecturers:	Univ.-Prof. Ph.D. Mathias Niepert
Type:	Exercise
Semester hours:	2
Teaching Language:	English
Semester:	SS 24
Organisation:	Maschinelles Lernen in den Simulationswissenschaften
Content:	Siehe Vorlesung
Link:	C@MPUS

Reinforcement Learning

Lecture
2 Sh
SS 24

Number:	021861000
Lecturers:	Univ.-Prof. Ph.D. Mathias Niepert
Type:	Lecture
Semester hours:	2
Teaching Language:	English
Semester:	SS 24
Organisation:	Maschinelles Lernen in den Simulationswissenschaften
Content:	Reinforcement Learning considers how an agent, interacting with a world, can improve or learn optimal behavior based on own experience or teacher demonstration. This branch of Artificial Intelligence and Machine Learning has become increasingly important foundation of robust intelligent systems and robotics. Optimal exploration (behavior that optimizes the agent's information gain) is a particularly interesting aspect of Reinforcement Learning. This lecture will introduce to the theory of Reinforcement Learning and then discuss state-of-the-art algorithms in this area. A focus of the lecture will be on deep reinforcement learning. Markov Decision Processes and Bellman's optimality principle basic model-free RL methods (policy gradient, Q-learning, etc) model-based RL methods offline reinforcement learning relational RL inverse RL, learning from demonstration and instruction basics of reinforcement learning theory transfer and multi-task learning applications
Link:	C@MPUS

Recent Applications of Machine Learning

Seminar
2 Sh
SS 24

Number:	021890000
Lecturers:	Univ.-Prof. Ph.D. Mathias Niepert Ho Minh Duy Nguyen
Type:	Seminar
Semester hours:	2
Teaching Language:	English
Semester:	SS 24
Organisation:	Maschinelles Lernen in den Simulationswissenschaften
Content:	https://www.f05.uni-stuttgart.de/informatik/studierende/seminaranmeldung/
Link:	C@MPUS

Machine Learning for Simulation Science

Colloquium
2 Sh
WS 23/24

Number:	021898003
Lecturers:	Univ.-Prof. Ph.D. Mathias Niepert
Type:	Colloquium
Semester hours:	2
Teaching Language:	German/English
Semester:	WS 23/24
Organisation:	Maschinelles Lernen in den Simulationswissenschaften
Content:	nur für Mitarbeiterinnen und Doktorandinnen
Link:	C@MPUS

Kolloquium Machine Learning for Simulation Science

Colloquium
2 Sh
SS 24

Number:	021898000
Lecturers:	Univ.-Prof. Ph.D. Mathias Niepert
Type:	Colloquium
Semester hours:	2
Teaching Language:	German
Semester:	SS 24
Organisation:	Maschinelles Lernen in den Simulationswissenschaften
Content:	nur für Mitarbeiter*innen und PhD-Studierende
Link:	C@MPUS

Grundlagen der Künstlichen Intelligenz

Exercise
1 Sh
WS 23/24

Number:	020412000
Lecturers:	Univ.-Prof. Ph.D. Mathias Niepert
Type:	Exercise
Semester hours:	1
Teaching Language:	English/German
Semester:	WS 23/24
Organisation:	Maschinelles Lernen in den Simulationswissenschaften
Content:	siehe Vorlesung
Link:	C@MPUS

Grundlagen der Künstlichen Intelligenz

Lecture
3 Sh
WS 23/24

Number:	020411000
Lecturers:	Univ.-Prof. Ph.D. Mathias Niepert
Type:	Lecture
Semester hours:	3
Teaching Language:	English
Semester:	WS 23/24
Organisation:	Maschinelles Lernen in den Simulationswissenschaften
Content:	Intelligenz Agentenbegriff Problemlösen durch Suchen, Suchverfahren Probleme mit Rand- und Nebenbedingungen Spiele Aussagen- und Prädikatenlogik Logikbasierte Agenten, Wissensrepräsentation Inferenz Planen Unsicherheit, probabilistisches Schließen Probabilistisches Schließen über die Zeit Entscheidungstheorie
Link:	C@MPUS

Deep Learning for the Sciences

Advanced Seminar
2 Sh
WS 23/24

Number:	021895000
Lecturers:	Univ.-Prof. Ph.D. Mathias Niepert Marimuthu Kalimuthu
Type:	Advanced Seminar
Semester hours:	2
Teaching Language:	English
Semester:	WS 23/24
Organisation:	Maschinelles Lernen in den Simulationswissenschaften
Content:	Deep learning has been successfully applied in a wide range of use cases and specifically in applications involving visual and textual data. Modern machine translation systems and search engines, for example, are using language models trained on large text corpora. Increasingly, deep learning is also applied to problems arising in the sciences and engineering. For instance, deep learning for graphs is used to learn simulators from data. The figure on the right shows a particle simulation obtained from a graph neural network trained on simulation traces from a numerical solver. The advantage of using deep learning in this context is its ability to integrate the simulation into a larger neural network with a corresponding loss function and train the resulting model end-to-end. Other applications of deep learning can be found in chemistry, the biomedical sciences, and engineering disciplines, where use cases range from drug-protein interaction prediction to modeling fluid dynamics. Since machine learning and specifically deep learning will be increasingly used in disciplines of science and engineering, this seminar’s goal is to provide an overview of applications, to give students a deeper understanding of recent work, and to have an opportunity to learn reading, analyzing, and engaging with scientific papers. If possible, the students are also encouraged to demo the application to the seminar.
Link:	C@MPUS

Deep Learning for the Sciences

Advanced Seminar
2 Sh
SS 24

Number:	021895000
Lecturers:	Univ.-Prof. Ph.D. Mathias Niepert Marimuthu Kalimuthu
Type:	Advanced Seminar
Semester hours:	2
Teaching Language:	English
Semester:	SS 24
Organisation:	Maschinelles Lernen in den Simulationswissenschaften
Content:	https://www.f05.uni-stuttgart.de/informatik/studierende/seminaranmeldung/
Link:	C@MPUS

Socially Intelligent Robotics Lab (Autonomous Systems)

Foundation Models

Exercise
2 Sh
WS 23/24

Number:	023800405
Lecturers:	Univ.-Prof. Dr. rer. nat. Steffen Staab Jun.-Prof. Dr.-Ing. Alina Roitberg Univ.-Prof. Ph.D. Mathias Niepert
Type:	Exercise
Semester hours:	2
Teaching Language:	English
Semester:	WS 23/24
Organisation:	Autonomous Sensing and Perception Systems
Content:	siehe Vorlesung
Link:	C@MPUS

Foundation Models

Lecture
2 Sh
WS 23/24

Number:	023800400
Lecturers:	Univ.-Prof. Dr. rer. nat. Steffen Staab Jun.-Prof. Dr.-Ing. Alina Roitberg Univ.-Prof. Ph.D. Mathias Niepert
Type:	Lecture
Semester hours:	2
Teaching Language:	German
Semester:	WS 23/24
Organisation:	Autonomous Sensing and Perception Systems
Content:	Foundation Models, such as GPT4 and Stable Diffusion, are models that are trained on very large amounts of data and can be effectively fine-tuned for a wide range of downstream tasks. This lecture aims to convey the key ideas behind the recent Large Language Models and Multimodal Foundation Models. The students will learn both, fundamental building blocks and optimization techniques, as well as advanced architectures and recent developments in the field of Foundation Models. Among others, the following topics will be covered: • Fundamentals: overview of the relevant building blocks and optimization techniques (e.g., transformer models, self-supervised learning), generative models. • Foundation models for text processing • Vision-based and multimodal foundation models • Discussion of ethical, societal, and legal facets of FMs Alongside the lectures, student projects are an integral part of this lecture. These projects focus on analysing and presenting recent research papers as well as implementing/using them for a specific use-case or application to gain hands-on experience. The students will present their results and write a report summarizing their learnings from the project.
Link:	C@MPUS

Automotive and Assistive Computer Vision

Exercise
2 Sh
SS 24

Number:	023800005
Lecturers:	Jun.-Prof. Dr.-Ing. Alina Roitberg
Type:	Exercise
Semester hours:	2
Teaching Language:	English/German
Semester:	SS 24
Organisation:	Autonomous Sensing and Perception Systems
Link:	C@MPUS

Automotive and Assistive Computer Vision

Lecture
2 Sh
SS 24

Number:	023800000
Lecturers:	Jun.-Prof. Dr.-Ing. Alina Roitberg
Type:	Lecture
Semester hours:	2
Teaching Language:	English/German
Semester:	SS 24
Organisation:	Autonomous Sensing and Perception Systems
Content:	Computer vision is a vital technology in both automotive innovations like autonomous driving and in developing assistive technologies such as assistive robotics or tools for guiding the visually impaired. Despite rapid progress linked to the rise of deep learning, CV faces significant challeng-es in real-world applications, including handling adverse conditions, reli-ance on large annotated datasets, computational demands, understanding decision-making processes and uncertainty quantification. This module explores the fundamentals and advanced developments in CV, addressing these challenges with a focus on applications in dynamic environments. By examining both automotive and assistive technologies, students will learn about the synergies and shared obstacles between these fields. The curriculum covers essential CV techniques, efficient architectures, and domain adaptation, complemented by hands-on projects to apply what they've learned in practical settings. Among others, the following topics will be covered: • Fundamental methods and tasks: overview of the relevant build-ing blocks and optimization techniques (e.g., CNNs, visual trans-former models), tasks relevant for intelligent cars, e.g., obstacle detection and scene understanding, perception inside and out-side the vehicle; and assistive technologies (CV for improved mobility, activity recognition, document analysis) • Uncertainty in computer vision, open set recognition, visual recognition with limited training data • Domain adaptation/generalization and robustness against distri-butional shifts • Sensor fusion • Efficient architectures • Discussion of SoA architectures and synergies between automo-tive and assistive technologies, long-term implications. Alongside the lectures, student projects are an integral part of this lecture. These projects focus on analysing and presenting recent research papers as well as implementing/using them for a specific use-case or application to gain hands-on experience. The students will present their results and write a report summarizing their learnings from the project.
Link:	C@MPUS

Teaching Overview

Analytic Computing

Oberseminar Artificial Intelligence

Machine Learning (Exercise for Engineering Students)

Machine Learning (Exercise for Engineering Scientists)

Machine Learning (Exercise for Data Science students)

Machine Learning

Machine Learning

Lab Course Artificial Intelligence: Deep Learning Lab

Knowledge Graphs

Knowledge Graphs

Intelligent Sensing and Perception

Advanced seminar on Foundations of Mobile Robotics

Machine Learning for Simulation Science

Theoretical and Methodological Foundations of Autonomous Systems

Theoretical and Methodological Foundations of Autonomous Systems

Reinforcement Learning

Reinforcement Learning

Recent Applications of Machine Learning

Machine Learning for Simulation Science

Kolloquium Machine Learning for Simulation Science

Grundlagen der Künstlichen Intelligenz

Grundlagen der Künstlichen Intelligenz

Deep Learning for the Sciences

Deep Learning for the Sciences

Socially Intelligent Robotics Lab (Autonomous Systems)

Foundation Models

Foundation Models

Automotive and Assistive Computer Vision

Automotive and Assistive Computer Vision

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