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Machine Learning: Methods and Tools

Course 0000003425 in WS 2020/1

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
Daniela Sanchez Lopera
Lorenzo Servadei
Rafael Stahl
Helmut Gräb

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 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;
Links E-Learning course (e. g. Moodle)
TUMonline entry

Equivalent Courses (e. g. in other semesters)

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