Master-Praktikum - Machine Learning in Crowd Modeling & Simulation (IN2106, IN4267)
Course 0000001145 in WS 2020/1
General Data
| Course Type | practical training |
|---|---|
| Semester Weekly Hours | 6 SWS |
| Organisational Unit | Informatics 5 - Chair of Scientific Computing (Prof. Bungartz) |
| Lecturers |
Felix Dietrich Kislaya Ravi |
| Dates |
Thu, 12:00–14:00, MI 02.07.023 and 2 singular or moved dates |
Assignment to Modules
-
IN2106: Master-Praktikum / Advanced Practical Course
This module is included in the following catalogs:- Further Modules from Other Disciplines
Further Information
Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.
| additional remarks | 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 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 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 modelling 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 |
|---|---|
| Links |
E-Learning course (e. g. Moodle) TUMonline entry |