Machine Learning: Methods and Tools
Course 0000003425 in WS 2020/1
General Data
Course Type | lecture with integrated exercises |
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Semester Weekly Hours | 4 SWS |
Organisational Unit | Chair of Electronic Design Automation (Prof. Schlichtmann) |
Lecturers |
Wolfgang Ecker Daniela Sanchez Lopera Lorenzo Servadei Rafael Stahl Assistants: Helmut Gräb |
Dates |
Assignment to Modules
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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 | In WS 2020/21 the course is planned to be presented online and asynchronous, i.e., download times of videos are at the discretion of participants. The exam is planned to be in written form and in physical presence (no warranty). **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; |
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Links |
E-Learning course (e. g. Moodle) TUMonline entry |