Machine Learning: Methods and Tools
Course 0000001908 in SS 2021
General Data
| Course Type | lecture with integrated exercises |
|---|---|
| Semester Weekly Hours | 4 SWS |
| Organisational Unit | Chair of Electronic Design Automation (Prof. Schlichtmann) |
| Lecturers |
Wolfgang Ecker Lorenzo Servadei Assistants: Helmut Gräb Rafael Stahl |
| Dates |
Fri, 13:00–14:00, virtuell |
Assignment to Modules
-
EI71040: Machine Learning: Methods and Tools / Machine Learning: Methods and Tools
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 | SS 2021: course and exam online. **Lecture, Exercises and hands-on lab** Digital transformation and machine learning; Python, standard libraries, SciPY and NumPy; Theory of Machine Learning, Regularization, Errors and Noise; Data analysis, pre-processing, visualization: Introduction to algorithms of Machine Learning; Introduction to Feedforward Neural Networks and Convolutional Neural Networks, RNNs, LSTM; Training of neural networks, attention models, unsupervised learning, reinforcement learning, hyper-parameter optimization; |
|---|---|
| Links |
E-Learning course (e. g. Moodle) TUMonline entry |