Physics-Informed Machine Learning
Course 0000003399 in SS 2023
General Data
| Course Type | lecture |
|---|---|
| Semester Weekly Hours | 2 SWS |
| Organisational Unit | Assistant Professorship of Multiscale Modeling of Fluid Materials (Prof. Zavadlav) |
| Lecturers |
Julija Zavadlav Koller |
| Dates |
Thu, 08:00–10:00, MW 1050 |
Assignment to Modules
-
MW2450: Physikbasiertes Machine Learning / Physics-Informed Machine Learning
This module is included in the following catalogs:- Catalogue of non-physics elective courses
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 | The module covers selected topics in machine learning ranging from introductory principles to new-fashioned techniques. Different areas and approaches in the field (supervised, unsupervised, and reinforcement learning, parametric vs. non-parametric, etc.) are introduced through recent success examples. The focus is on (i) models for classification and regression (linear regression, Bayesian Uncertainty Quantification and model selection, regularization and sparsity aware learning, deep neural networks, stochastic gradient descent), (ii) models for clustering and dimensionality reduction (k-means, PCA, autoencoders, self-organizing maps), and (iii) generative models (variational autoencoders, generative adversarial networks). |
|---|---|
| Links |
Course documents E-Learning course (e. g. Moodle) Additional information TUMonline entry |
Equivalent Courses (e. g. in other semesters)
| Semester | Title | Lecturers | Dates |
|---|---|---|---|
| SS 2022 | Physics-Informed Machine Learning |
Thu, 08:00–10:00, MW 1050 |
|
| SS 2021 | Physics-Informed Machine Learning | ||
| SS 2020 | Physics-Informed Machine Learning |