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)

Prof. (FH) Dipl.-Informatiker Karsten Böhm

recommended optional program components


course unit code


type of course unit

integrated lecture

mode of delivery


work placement(s)