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Introduction to Scientific Machine Learning for Engineers (MW2435)

Course 0000002392 in WS 2020/1

General Data

Course Type lecture
Semester Weekly Hours 2 SWS
Organisational Unit Chair of Aerodynamics and Fluid mechanics (Prof. Adams)
Lecturers Nikolaus Adams
Ludger Pähler
Dates Thu, 09:30–11:00

Assignment to Modules

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 This course presents the fundamentals of machine learning building on a basic understanding of linear algebra and the axiomatic description of probability theory. Starting with supervised learning basic regression approaches are being discussed, culminating in generalized linear models. Starting with support vector machines various kernel approaches such as Gaussian processes are then covered. We subsequently move on to the general class of neural network methods, their training via backpropagation, bias vs. variance trade-offs, regularization and modern classes of neural networks. The classes covered in this course are recurrent neural networks, convolutional neural networks, generative adversarial networks, and the more modern transformer networks. The course subsequently culminates in an introduction to variational inference, autoencoders and principal component analysis. The content is subject to change based on progress during the semester.
Links E-Learning course (e. g. Moodle)
Additional information
TUMonline entry
TUMonline registration

Equivalent Courses (e. g. in other semesters)

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