- Trainer/in: Xavier Genereux
In this lab, students will work in small teams to implement algorithms and techniques from the field of Process Mining. The goal is to gain hands-on experience with modern process analysis methods and to deepen understanding by translating theoretical concepts into working software.
Possible topics (to be finalized at the beginning of the course):
- LLM-assisted Process Mining Techniques\
- Event Abstraction and Transformation
- Clustering in Process Mining
- Partially-Ordered Event Data
Technical Requirements:
- Solid programming skills in Python are mandatory (the lab will primarily use the PM4Py library)
- For visualization tasks, knowledge of web technologies (HTML, CSS, JavaScript, TypeScript) is helpful
Language:
The lab will be held in English or German, depending on the composition of the participants.
Publication Opportunity:
Depending on student interest and motivation, project results may be further developed and submitted as a tool paper to an international conference upon completion of the lab.
- Trainer/in: Daniel Schuster
Kurzbeschreibung
Dieses Praktikum, das einen Umfang von 12 ECTS (!) hat, vermittelt die Fähigkeit, Anwendungsfälle aus den Bereichen der Optimierung und dem maschinellen Lernen für Quantencomputer zu modellieren und darüber hinaus einen Einstieg in die praktische Arbeit mit existierenden Quantencomputern. Dafür stehen im QAR-Lab derzeit vier Rechner zur Verfügung: Das IBM Q System Two, der IonQ Aria, der Fujitsu DAU und der D-Wave Advantage. In Kooperation mit namhaften Partnern aus der Industrie werden Aufgabenstellungen mit starker Relevanz für praktische Anwendungen vergeben. Studierende haben in Gruppen die Möglichkeit, je eine Aufgabenstellung auf verschiedenen Rechnern auszuführen und zu vergleichen. Das Praktikum schließt mit einer Präsentation der Ergebnisse vor unseren Industriepartnern ab.
Inhalt des Praktikums
Quantencomputing ermöglicht effizientere Ansätze zur Lösung zentraler Probleme der Informatik durch die Nutzung quantenmechanischer Effekte. Mit der zunehmenden Größe und Qualität aktueller Quantencomputer ist es bereits heute möglich diesen Quantenvorteil in der Praxis nachzuweisen. Die Herausforderung besteht im Allgemeinen darin mit den im Quantencomputing zusätzlich zur Verfügung stehenden algorithmischen Bausteinen Lösungsverfahren zu entwickeln, die einen anwendungsrelevanten Quantenvorteil ermöglichen.
Dieses Praktikum stellt eine Einführung in den anwendungsorientierten Einsatz von Quantencomputing dar. Hierbei werden Ansätze aus dem Bereich Quantenoptimierung zur Lösung praxisrelevanter Probleme konzipiert, implementiert und analysiert. Dabei kommt „echte“ Quantenhardware der Hersteller IBM, IonQ, Fujitsu und D-Wave Systems zu Einsatz.
Eine Auswahl der behandelten Themen lautet:
- Grundlagen des Quantencomputings
- Mathematische Modellierung
- Optimierung
- Quantum Annealing
- Quantenoptimierungsalgorithmen
- Einführung in verschiedene QC-Plattform SDKs
Ablauf & Prüfung
Das Praktikum gliedert sich in zwei Phasen: In der dreiwöchigen Theoriephase werden Grundlagenkenntnisse vermittelt, während in der Praxisphase (startend ab der vierten Woche) in Gruppen an jeweils einer Aufgabenstallung gearbeitet wird. Die Gruppeneinteilung und Themenvergabe findet voraussichtlich Ende der 3. Semesterwoche statt.
Im Rahmen der Projektphase wird pro Gruppe eine ca. zehnseitige wissenschaftliche Arbeit erstellt, die insbesondere die eigene Methodik und erzielte Ergebnisse beinhaltet. Das Praktikum schließt mit einer benoteten Präsentation der Ergebnisse ab.
Termine
Wöchentlich Dienstags, voraussichtlich 10-12 Uhr, Ort TBA, und Donnerstags, voraussichtlich 14-16 Uhr, Ort TBA, sowie Zusatztermine mit den Betreuern bei Bedarf, remote / in den Räumen des Lehrstuhls. Bei einer großen Anzahl der Termine besteht Anwesenheitspflicht. (Details folgen auf der Veranstaltungswebseite des Lehrstuhls.) (Beginn: 14.10.2025, Ende: 05.02.2026, bis auf ggf. später stattfindende mündliche Prüfung.)
Veranstaltungswebseite: TBA
- Trainer/in: Claudia Linnhoff-Popien
- Trainer/in: Jonas Stein
- Trainer/in: Maximilian Zorn
Even though large models nowadays e.g. GPT-4, LLaVA, LLaMA, Qwen already yield impressive performances and can assist people in daily (vision) language tasks, there are certain issues that remain to be solved. Mismatching with humans in the real world is one of the most critical issues, where models may have different confidence levels compared with humans, or yield social bias related to gender, race, etc, or generate unsafe contents which could do harm to users. In this class, we will introduce basic knowledge of large models, especially large vision-language models, and the related alignment issues and methods. Students will have a chance to implement projects related to large models, and study recent scientific papers.
Language: English
Pre-requirement: Basic Knowledge in Python, deep learning. Self-motivated to learn recent advances in Large multimodal models.
- Trainer/in: Jian Lan
The Binary Analysis with AI Praktikum invites students to explore the fascinating intersection of binary analysis and artificial intelligence with a research-oriented perspective. This praktikum aims not only to provide a solid foundation in binary analysis and machine learning, but also to expose students to some of the current research challenges in learning-based binary analysis and encourage independent experimentation.
By the end of this praktikum, students are expected to have a clear understanding of the state-of-the-art problems in learning-based binary analysis and to have conducted their own initial explorations in tackling these problems.
Language: English
Pre-requisites:
Basic knowledge of machine learning
Familiarity with binary analysis concepts (e.g., disassembly, control flow analysis) is helpful but not mandatory
Proficiency in Python (experience with libraries such as NumPy, PyTorch, or TensorFlow is a plus)
- Trainer/in: Johannes Kinder
- Trainer/in: Yunru Wang
Contents
The practical course builds on the lecture Introduction to Quantum Computing and is organized with LRZ and DLR.
The topic of the practical course is simulation of quantum circuits. You are going to learn different methods to classically simulate quantum circuits. Based on those you are going to implement and optimize your own simulator in groups of two.
Participation
- Requirements: Successful participation on the lecture Introduction to Quantum Computing at LMU or a similar course at another university.
- Audience: The practical course is aimed at students in the Master's degree program in Computer Science, Media Informatics, Bioinformatics, students in the main study program in Computer Science (Diploma) or Media Informatics (Diploma) as well as students with a minor in Computer Science. Bachelor's students of Computer Science or Media Informatics can specify the lecture as "Vertiefende Themen der Informatik für Bachelor".
- SWS/ECTS: 4 SWS, 6 ECTS according to module description
- Major course assessment: Presentation + Oral Exam
- Participation Restriction: The maximum number of participants is 14.
Important:
Attendance is compulsory during the block practical and punctuality is expected.
Failure to attend will be reported to the examination office with a grade of 5 (failed).
Being late multiple times will have a negative effect on your grade.
The course will be held in English.
- Trainer/in: Florian Krötz
- Trainer/in: Korbinian Staudacher
- Trainer/in: Xiao-Ting To
Dieses Praktikum wird in Zusammenarbeit mit Dr. Marco Maier als Lehrbeauftragtem an der LMU München durchgeführt. Dr. Marco Maier hat an der LMU am Institut für Informatik promoviert und ist aktuell CTO bei TAWNY (www.tawny.ai), einem der spannendsten Affective-Computing-Startups in Europa. Studenten erhalten in diesem Praktikum wertvolle Einblicke sowohl in das Forschungsgebiet als auch in den neuesten Stand der Technik in der Industrie.
Voraussetzungen: Kenntnisse bzw. mindestens ausgeprägtes Interesse an den Themen Artificial Intelligence, Machine Learning, etc.; Progammierkenntnise in Python vorteilhaft.
- Trainer/in: Philipp Altmann
- Trainer/in: Marco Maier
The Master Praktikum Mobile Application Development gives an introduction to Kotlin and modern application development for Android. Participants will explore state-of-the-art frameworks, tools, and concepts to create their own Android Application. The Praktikum contains mandatory homework and a final project concluded by a presentation and code review.
- Trainer/in: Balazs Toth
Die Themen und Aufgaben sind angelehnt an das Schichtenkonzept bei Netz-Protokollen angelehnt. Im Verlauf des Praktikums werden die verschiedenen Abstraktionsschichten untersucht und auf Details einzelner Protokolle eingegangen.
Zusätzlich werden die Inhalte um moderne Ansätze wie zum Beispiel Software Defined Networks (SDNs) ergänzt.
Nach Absolvierung des Praktikums können die Teilnehmer sich zügig in die Konfiguration bestehender Infrastrukturen einarbeiten und bei deren effizienter Administration unterstützen.
Übersicht der Themengebiete:
- Aufbau von IPv4- und IPv6-Netzen
- Virtuelle LANs (VLANS)
- Wegewahl/Vermittlung inter- und intra-AS
- Hilfsprotokolle
- Netzmanagement
- Software Defined Networks
Zeit: Di 14:00-16:00 (wöchentlich)
- Trainer/in: Daniel Diefenthaler
- Trainer/in: Fabian Dreer
Place: Akademiestr. 7, 1st floor, Room 105
Time: by arrangement
6 SWS
Language: English
Recommended Prerequisites for Students:
· Strong programming skills
· Prior practical experience in machine learning, typically acquired through one of the standard ML courses
Modern Deep Learning has fundamentally changed Artificial Intelligence. Novel applications as well as significant improvements to old problems continue to appear at a staggering rate. Especially the areas of image and video understanding, retrieval, and synthesis have seen tremendous improvements and even the human baseline has been outperformed in several difficult applications.
The algorithms and the fundamental research in deep Machine Learning and Computer Vision that are driving this revolution are improving at an ever-increasing rate. The goal of this practical lab is, therefore, to give students hands-on experience with the state-of-the-art in this field of research. We will work on current problems in Computer Vision and Machine Learning and build on current algorithms to practically implement novel solutions. Consequently, the practical is also a good opportunity to take a close look at this area of research and prepare for a potential future final thesis.
Topics include but are not limited to:
* Image & video synthesis
* Visual superresolution and Image completion
* Artistic style transfer
* Interpretability of deep models
* Visual object classification, detection, tracking
* Self-supervised learning
* Metric and representation learning
* Modern deep learning approaches, such as transformers and self-attention, invertible neural networks, etc.
* …
- Trainer/in: Ursula Fantauzzo
- Trainer/in: Felix Krause
- Trainer/in: Björn Ommer
Place: Akademiestr. 7, 1st floor, Room 105
6 SWS
Language: English
Time: To be announced on this page
Recommended Prerequisites for Students:
· Strong programming skills
· Prior practical experience in machine learning, typically acquired through one of the standard ML courses
Modern Deep Learning has fundamentally changed Artificial Intelligence. Novel applications as well as significant improvements to old problems continue to appear at a staggering rate. Especially the areas of image and video understanding, retrieval, and synthesis have seen tremendous improvements and even the human baseline has been outperformed in several difficult applications.
The algorithms and the fundamental research in deep Machine Learning and Computer Vision that are driving this revolution are improving at an ever-increasing rate. The goal of this practical lab is, therefore, to give students hands-on experience with the state-of-the-art in this field of research. We will work on current problems in Computer Vision and Machine Learning and build on current algorithms to practically implement novel solutions. Consequently, the practical is also a good opportunity to take a close look at this area of research and prepare for a potential future final thesis.
Topics include but are not limited to:
* Image & video synthesis
* Visual superresolution and Image completion
* Artistic style transfer
* Interpretability of deep models
* Visual object classification, detection, tracking
* Self-supervised learning
* Metric and representation learning
* Modern deep learning approaches, such as transformers and self-attention, invertible neural networks, etc.
* …
- Trainer/in: Ursula Fantauzzo
- Trainer/in: Felix Krause
- Trainer/in: Björn Ommer
The satisfiability problem for classical propositional logic (SAT) is the canonical NP-complete problem, therefore algorithms for solving it are of essential importance for theoretical computer science, their development and study forms an entire area of research.
Recently there has been a lot of progress in this area, in theory as well as in practice. On the theoretical side, there are new algorithms for which worst-case upper bounds on the run-time can be proven, that are better than the obvious O(2^n) for brute-force search. The current best such (probabilistic) algorithms have a run-time of O(1.307^n) for 3-SAT and O(1.469^n) for 4-SAT.
On the other hand, there are very good heuristics and implementations for the classic backtracking algorithms (DPLL), which can solve even very large inputs with hundred thousands of variables and millions of clauses in reasonably short time. These so-called SAT solvers have by now such a good performance that they are routinely used in applications for the solution of combinatorial optimization problems.
Lecture: Tuesdays, 14-16, room 114, Amalienstr. 73a, weekly, beginning October 14.Tutorials: Fridays, 14-16, room 114, Amaliestr. 73a, on demand, starting October 24.
Attendance is mandatory. To obtain the credits for the course, the participants will have to successfully complete three implementation projects specified in the problem sets. These projects are done in teams of 3 participants. After each due date, there will be an oral presentation and review of the projects.
- Trainer/in: Jan Johannsen