Data Science & Intelligent Analytics PT
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Machine Learning & Deep Learning

level of course unit

Master's course

Learning outcomes of course unit

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

prerequisites and co-requisites

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)

course contents

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.

recommended or required reading

- 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)

assessment methods and criteria

Project documentation and presentation

language of instruction


number of ECTS credits allocated


eLearning quota in percent


course-hours-per-week (chw)


planned learning activities and teaching methods

The following methods are used:

- Processing of exercises
- Interactive workshop

semester/trimester when the course unit is delivered


name of lecturer(s)

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

recommended optional program components


course unit code


type of course unit

integrated lecture

mode of delivery


work placement(s)