Apply
now
now
Machine Learning & Deep Learning
level of course unit
Master's courseLearning 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
- 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.
- 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
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)
- 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
Englishnumber of ECTS credits allocated
10eLearning quota in percent
25course-hours-per-week (chw)
4planned learning activities and teaching methods
The following methods are used:
- Processing of exercises
- Interactive workshop
- Processing of exercises
- Interactive workshop
