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
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)
assessment methods and criteria
Project documentation and presentation
language of instruction
English
number of ECTS credits allocated
10
eLearning quota in percent
25
course-hours-per-week (chw)
4
planned learning activities and teaching methods
The following methods are used:
- Processing of exercises
- Interactive workshop
semester/trimester when the course unit is delivered
2
name of lecturer(s)
Dr. Michael Hecht, Prof. (FH) Dr. Michael Kohlegger
recommended optional program components
none
course unit code
MLAL.3
type of course unit
integrated lecture
mode of delivery
Compulsory
work placement(s)
none