Master-Praktikum - Machine Learning in Crowd Modeling & Simulation (IN2106, IN4267)
Lehrveranstaltung 0000001145 im WS 2019/20
Basisdaten
LV-Art | Praktikum |
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Umfang | 6 SWS |
betreuende Organisation | Informatik 5 - Lehrstuhl für Scientific Computing (Prof. Bungartz) |
Dozent(inn)en |
Felix Dietrich Fabio Gratl |
Termine |
Do, 12:00–14:00, MI 02.07.023 sowie 2 einzelne oder verschobene Termine |
Zuordnung zu Modulen
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IN2106: Master-Praktikum / Advanced Practical Course
Dieses Modul ist in den folgenden Katalogen enthalten:- weitere Module aus anderen Fachrichtungen
weitere Informationen
Lehrveranstaltungen sind neben Prüfungen Bausteine von Modulen. Beachten Sie daher, dass Sie Informationen zu den Lehrinhalten und insbesondere zu Prüfungs- und Studienleistungen in der Regel nur auf Modulebene erhalten können (siehe Abschnitt "Zuordnung zu Modulen" oben).
ergänzende Hinweise | Human crowd movement is comprised of highly complex dynamical systems, with hundreds or even thousands of acting and reacting participants that show several emergent behavior patterns. The scientific understanding of crowds involves topics ranging from mathematical modeling via algorithm development for machine learning (ML) from experimental and simulated data as well as the implementation of simulation software up to research in psychology and sociology. Participants in this lab course will learn about the core mechanics in human movement and interactions in crowds. The current state of the art in mathematical modeling will be discussed, and students will implement models in several exercises. As a reference, the students will be introduced to the crowd simulation software VADERE (www.vadere.org). After this introduction to the modeling of crowds, we will discuss current machine learning approaches to analyze the simulated results, as well as experimental data. Techniques from statistics, dynamical systems theory, manifold learning, and numerical analysis will be introduced in short lectures, implemented by the students, and then used by them to analyze their own simulation results from previous exercises. In a final project, the participants can choose to focus on their own model of a crowd, a specific aspect of crowd simulation, or a particular technique in ML to analyze simulation or experimental data. All exercises and the final project are designed for groups of three to four students. This results in a course size of about 30 students, which can be extended up to 40 by increasing the group size. The lab course covers the following topics: • Introduction to the modeling of crowds • Introduction to dynamical systems theory • Introduction to appropriate ML techniques • Numerical analysis of complex systems • Implementation of simulation software extensions and validation of models to data • Implementing ML techniques with application to simulation software results |
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Links |
E-Learning-Kurs (z. B. Moodle) TUMonline-Eintrag |