Technology-Enhanced Learning (TEL) describes the integration or application of technologies in the context of teaching and learning.
This master lecture gives an overview of e-learning topics from the perspective of computer science. General didactic scenarios and learning theories are discussed first. Based on this theoretical basis, tools, platforms, architectures and standards as well as special use cases (e-assessment, mobile learning, collaborative learning and the like) are covered. Furthermore, related non-technical aspects such as organization, rights, business models, etc. are discussed.
The lecture will be held in presence.
Form of examination: Written or oral examinations
- Teacher: Armin Egetenmeier
- Teacher: Sven Strickroth

- Teacher: Moritz Dannehl
- Teacher: Sebastian Eckl
- Teacher: Matías Gobbi
- Teacher: Johannes Kinder
Automated theorem proving is a subfield of mathematical logic that concerns itself with proving mathematical theorems fully automatically using computer programs. These programs are called automated theorem provers. They can be used as standalone programs to solve logic problems or in tandem with interactive theorem provers (also called proof assistants) to discharge proof obligations that arise in interactive proofs.
In this course, we will review some of the main approaches to automated theorem proving. The course focuses on the theory of theorem proving. Stylistically, the course has a mathematical flavor (with definitions, lemmas, proofs, etc.).
The course is based on the materials from Dr. Uwe Waldmann’s courses Automated Reasoning I and Automated Reasoning II at Saarland University. We are grateful to him for letting us use his materials.
- Teacher: Jasmin Blanchette
- Teacher: Lydia Kondylidou
Computational Intelligence is the study of algorithms that exhibit (seemingly) intelligent behavior. Historically, it encompasses several fields of computer science, including logic, optimization, and multi-agent system. Our goal is to give the recently rapid developments in artificial intelligence a broader context by connecting them to their historical and scientific foundations. Thus, in this course we discuss intelligent system specified in an increasingly complex manner as well as relevant examples and literature.
- Teacher: Thomas Gabor
- Teacher: Maximilian Zorn
Mit der rasanten Verbreitung von Netztechnologien und -diensten sowie deren Durchdringung des privaten wie des geschäftlichen Bereichs steigt der Bedarf an sicheren IT-Systemen. Immer häufiger auftretende Angriffe auf vernetzte IT-Systeme mit zum Teil extrem hohem wirtschaftlichen Schaden für die betroffenen Firmen verdeutlichen den Bedarf nach wirksamen Sicherheitsmaßnahmen.
Diese Vorlesung beschäftigt sich mit ausgewählten Sicherheitsanforderungen und -mechanismen und deren Umsetzung in verteilten Systemen. Themen sind unter anderem:
- Information Security Management (ISO/IEC 27001)
- Bedrohungen und Angriffes
- Kryptographische Algorithmen
- Sicherheitsmechanismen und deren Realisierung
- Netz-Sicherheit
Hörerkreis
Die Vorlesung richtet sich an Studierende im Masterstudiengang Informatik, Medieninformatik oder Bioinformatik. Studierende der Informatik bzw. Medieninformatik im Bachelor können die Vorlesung als "Vertiefende Themen der Informatik für Bachelor" angeben.Voraussetzungen: Vorlesung Rechnernetze (dringend empfohlen), Vorlesung Betriebssysteme (empfohlen)
- Teacher: Amineh Akhavan Saraf
- Teacher: Katharina Novikov
- Teacher: Helmut Reiser
- Teacher: Daniel Weber
The course will be taught in English.
The course will provide an overview of advanced topics in computer graphics not currently covered by other LMU courses. In particular:
- Emphasis on real-time algorithms suitable for videogames or interactive exploration of virtual worlds.
- Realistic graphics: Advanced effects not present in off-the-shelf solutions; physical phenomena and implementation possibilities as algorithms.
- Non-photorealistic graphics (for illustrations or for artistic effects); especially real-time effects.
- Introduction to proofs of computational time limits and convergence to the correct solution.
- Remote visualization advantages, disadvantages and implementation details.
- Virtual reality: Caveats and available resources (content of course orthogonal to the subject Virtual Reality (2019, Vorlesung; 2021, Praktikum).
- Applications in Serious games.
This subject does not count towards a Vertiefende Themen der Informatik/Medieninformatik für Bachelor.
- Teacher: Ruben Garcia-Hernandez
Welcome to the course webpage for Parallel and High Performance Computing for winter term 2025/26 at LMU Munich. Here you will find details on the lecture accompanying practical lab exercises.
Lecture slides will be made available chapter-by-chapter through LSF.
Material of the lab exercises will be available via Moodle.
Here is the enrollment key: ParallelPioneers
Content
Parallel computing is concerned with using multiple compute units to solve a problem faster or with higher accuracy. Historically, the main application area for parallel machines is found in engineering and scientific computing, where high performance computing (HPC) systems today employ tens- or even hundreds of thousand compute cores.
The application area for parallel computing has, however, expanded recently to essentially include all areas of information technology. Virtually all servers, desktop, and notebook systems, and even smartphones and tablets are today equipped with CPUs that contain multiple compute cores. In each case, the potential for these systems can only be fully realized by explicit parallel programming. As such, understanding the benefits, challenges, and limits of parallel computing is increasingly becoming a "must have" qualification for IT professionals.
This course addresses the increasing importance of parallel and high performance computing and is covering three interwoven areas: Parallel hardware architectures, parallel algorithm design, and parallel programming. The successful student will be able to identify potentials for parallel computing in various application areas, judge the suitability of contemporary hardware architectures for a parallel computing problem and understand efficient implementation strategies using modern parallel programming approaches.
The lecture is partially based on material that has been developed at UC Berkeley and which has been funded by the US National Science Foundation. The course slides will be made available for download by the date of the lecture and will be in English.Audience
The course is intended for both bachelor and master students of computer science and related fields. More formally, in German: Die Vorlesung richtet sich an Studenten der Informatik bzw. Medieninformatik (Diplom) nach dem Vordiplom sowie an Studenten der Informatik, Bioinformatik bzw. Medieninformatik (Bachelor, Master) im Rahmen der vertiefenden Themen der Informatik. Für Vorlesung und Übung werden 6 ECTS-Punkte vergeben.Lab Exercises
The lecture is accompanied by a lab exercises to deepen the understanding of topics covered in the lecture. High performance computing systems hosted at the Leibniz Supercomputing Center will be made available to the students. Worksheets for the lab exercises will be made available on Moodle.
- Teacher: Sergej-Alexander Breiter
- Teacher: Karl Fürlinger
Die Vorlesung behandelt die folgende Themen:
- Optimal Scoring Subsequences
- Suffix-Trees Revisited
- Repeats
- Interludium: LCA-Queries und RMQ
- Suffix-Arrays
- Genome Rearrangements
- Teacher: Caroline Friedel
Grid- und Cloud-Computing repräsentieren zwei zentrale Ausprägungen verteilter IT-Infrastrukturen. Sie ermöglichen den bedarfsorientierten Zugriff auf Rechenleistung, Speicher, Plattformen oder Visualisierungsdienste, die über geografisch und organisatorisch verteilte Systeme bereitgestellt werden. Im Zentrum stehen dabei virtualisierte, dynamisch skalierbare Ressourcen, die flexibel über das Internet genutzt werden können.
Die Vorlesung mit begleitenden Übungen vermittelt die theoretischen Grundlagen und praktischen Aspekte dieser Systeme. Nach einer Einführung in Motivation, Anwendungsfelder und grundlegende Modelle verteilter Systeme werden zentrale Basistechnologien erläutert. Darauf aufbauend widmet sich die Veranstaltung aktuellen Architekturen und Plattformen im Cloud-Computing sowie der Entwicklung und Ausführung verteilter Anwendungen mithilfe moderner Cloud-Dienste und datengetriebener Softwareumgebungen. Auch der Einsatz von kommerziellen Cloud-Anbietern mit internationalen Rechenzentren sowie der Aufbau verteilter Dateninfrastrukturen wird behandelt. Zusätzlich werden Konzepte virtueller Organisationen, Grid-Infrastrukturen und Verfahren zum Ressourcen- und Datenmanagement thematisiert.
Aspekte wie die Integration von Cloud-Technologien in allgegenwärtige digitale Umgebungen – etwa im Kontext des Internet der Dinge – werden ergänzend angesprochen. Auch hybride Konzepte wie Grids of Clouds und Clouds of Grids werden vorgestellt. Abschließend diskutieren wir besondere Herausforderungen verteilter Systeme in Echtzeitszenarien, etwa im Urgent Computing, sowie aktuelle Entwicklungen und Zukunftstrends.
Die Veranstaltung richtet sich primär an Masterstudierende, die sich mit modernen Konzepten des verteilten Hochleistungsrechnens beschäftigen möchten. Besondere Aufmerksamkeit gilt dabei Aspekten der Systemarchitektur, der Programmierung, der Leistungsoptimierung sowie der Energieeffizienz.
Zur Vertiefung und Erweiterung des Blickwinkels tragen Gastvorträge externer Expertinnen und Experten bei, die Einblicke in aktuelle Forschungs- und Anwendungsfelder geben. Einige dieser Beiträge werden in englischer Sprache gehalten.
- Teacher: Maximilian Höb
- Teacher: Dieter Kranzlmüller
Im Rahmen dieser Vorlesung werden Inhalte rund um das Thema Virtual Reality vermittelt. Die Veranstaltung besteht aus einem Vorlesungs- und einem Übungsteil. Die Vorlesung fokussiert sich auf die Präsentation theoretischer Inhalte, die im Rahmen der Übung vertieft werden.
- Teacher: Elisabeth Mayer
- Teacher: Thomas Odaker
Enrollment Key: VerParProg 25/26
The course deals with mostly automatic verification approaches for multi-threaded programs with shared memory. Topics of the course are:
- Semantics of parallel programs, e.g., interleaving semantics
- Static and dynamic approaches for data race detection
- Techniques for deadlock detection
- Verification of program properties (e.g., with sequentialization, bounded model checking, etc.)
- Partial Order Reduction
- Thread-modular verification
At the end of the course, students can name a number of techniques for the verification of parallel programs, especially in the area of data race and deadlock detection as well as for verification of safety properties. They should be able to explain the underlying formalisms of the techniques, to describe the work flow of the different techniques, and to apply the techniques on examples. Moreover, the students know the strengths and weaknesses of the techniques.
Literature
Program Semantics
- K. R. Apt, F. S. de Boer, E.-R. Olderog: Verification of Sequential and Concurrent Programs. Springer 2009.
Data Race Detection
- S. Savage, M. Burrows, G. Nelson, P. Sobalvarro, T. E. Anderson: Eraser: A Dynamic Data Race Detector for Multi-Threaded Programs. SOSP 1997.
- E. Poznianski, A. Schuster: Efficient On-the-Fly Data Race Detection in Multithreaded C++ Programs. IPDPS 2003.
- C. Flanagan, S. N. Freund: FastTrack: efficient and precise dynamic race detection. PLDI 2009.
Deadlock Detection
- Dawson R. Engler, Ken Ashcraft: RacerX: effective, static detection of race conditions and deadlocks. SOSP 2003.
- Mayur Naik, Chang-Seo Park, Koushik Sen, David Gay: Effective static deadlock detection. ICSE 2009.
Sequentialization
- Akash Lal, Thomas W. Reps: Reducing Concurrent Analysis Under a Context Bound to Sequential Analysis. CAV 2008.
- Omar Inverso, Ermenegildo Tomasco, Bernd Fischer, Salvatore La Torre, Gennaro Parlato: Bounded Model Checking of Multi-threaded C Programs via Lazy Sequentialization. CAV 2014.
Bounded Model Checking
- L. C. Cordeiro, B. Fischer: Verifying multi-threaded software using SMT-based context-bounded model checking. ICSE 2011
Thread-modular Verification
- C. Flanagan, S. N. Freund, S. Qadeer: Thread-Modular Verification for Shared-Memory Programs. ESOP 2002
Partial Order Reduction
- D. Peled: Partial-Order Reduction. Handbook of Model Checking 2018.
- P. Godefroid, D. Pirottin: Refining Dependencies Improves Partial-Order Verification Methods (Extended Abstract). CAV 1993.
- P. Godefroid, P. Wolper: Using Partial Orders for the Efficient Verification of Deadlock Freedom and Safety Properties. Formal Methods in System Design 2(2) 1993.
Atomicity Checking
- C. Flanagan, S. N. Freund: Atomizer: a dynamic atomicity checker for multithreaded programs. POPL 2004
- Teacher: Marie-Christine Jakobs
- Teacher: Márk Somorjai
This lecture will be held in english.
Artificial intelligence uses ideas and concepts from different disciplines, such as neuroscience, cognitive science, mathematics and engineering. In recent years, machine learning techniques, a subfield of artificial intelligence, have achieved impressive success in applications such as image categorization, face or speech recognition, language processing, and control problem solving. The goal of the course is to understand the fundamentals of intelligent systems, focusing on an interplay between application (practice) and mathematical background (theory).
A selection of the topics covered is:
- Basic and advanced techniques of intelligent systems
- Optimization
- Intelligent systems in practice
- Multi-agent systems
- Foundation models
- Teacher: Jonas Nüßlein
During the last decade, the
availability of large amounts of data and the substantial increase in
computing power allowed a renaissance of neural
networks and advanced planning techniques for independent agents.
Whereas deep learning extended well-established neural
network technology to allow a whole new level of data transformation,
modern reinforcement learning techniques yield the artificial backbone
for intelligent assistant systems and autonomous vehicles. The course
starts with an introduction to neural networks and explains the
developments that led to deep architectures. Furthermore, the course
introduces advanced planning techniques and how they can
be trained using deep neural networks and other machine learning
technologies.
The enrollment key will be announced in the lectures.
- Teacher: Zongyue Li
- Teacher: Philipp Pfefferkorn
- Teacher: Matthias Schubert