Machine Learning & Deep Learning

Master's course

The following skills are developed in the course: - Students are familiar with tools (e.g. libraries, cloud platforms or software tools), with which machine learning can be supported. - Students can compare the tools developed with regard to their suitability for specific problems. - Students can design end-to-end machine learning projects. - Students can carry out end-to-end machine learning projects independently

1st semester: Students have previous knowledge of mathematics/statistics up to 8 ECTS and therefore know simple statistical measures as well as basic statistical test procedures (e.g. t-test). / 2nd semester: No prerequisites / 2nd semester: Module examination MLAL.A1 (Algorithmic 1)

The following content is discussed in the course: - Classical neural networks as a supplement to classical algorithms of data science (e.g. Random Forests, SCM, etc.) - Fallen, artificial neural networks (CNN) - Recursive, artificial neural networks (RNN, LSTM) - Continuing, artificial neural networks (GAN, FARM, BERT, CGAN, etc.) The network types discussed are subject to constant change. For this reason, only a few network types are men-tioned here as examples. Current network types are also discussed and applied in the course.

PRIMARY LITERATURE: - Géron, A. (2017): Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques for Building Intelligent Systems (Ed. 1), O´Reilly, Farnham (ISBN: 978-1491962299)

Project documentation and presentation

English

10

25

4.0

The following methods are used: - Processing of exercises - Interactive workshop

2

Dr. Michael Hecht, Prof. (FH) Dr. Michael Kohlegger

MLAL.3

integrated lecture

Compulsory

none