Intelligent & Adaptive Systems (E)
Niveau
second cycle, Master
Learning outcomes of the courses/module
The participants:
• have a basic understanding of Machine Learning
• are familiar with tools (e.g. libraries, cloud platforms or software tools), with which machine learning can be supported.
• can compare the tools developed with regard to their suitability for specific problems.
• can design simple machine learning projects.
• can carry out simple machine learning projects independently
• have a basic understanding of Machine Learning
• are familiar with tools (e.g. libraries, cloud platforms or software tools), with which machine learning can be supported.
• can compare the tools developed with regard to their suitability for specific problems.
• can design simple machine learning projects.
• can carry out simple machine learning projects independently
Prerequisites for the course
Basic knowledge in programming and mathematics/statistics
Course content
The following skills are developed in the course:
- Students have a basic understanding of Machine Learning
- 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 simple machine learning projects.
- Students can carry out simple machine learning projects independently
- Students have a basic understanding of Machine Learning
- 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 simple machine learning projects.
- Students can carry out simple machine learning projects independently
Recommended specialist literature
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)
- Lutz, M (2013): Learning Python (Ed. 1), O'Reilly Media, Farnham (ISBN: 978-1449355739)
- 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)
- Lutz, M (2013): Learning Python (Ed. 1), O'Reilly Media, Farnham (ISBN: 978-1449355739)
Assessment methods and criteria
Exam
Language
English
Number of ECTS credits awarded
6
Share of e-learning in %
Semester hours per week
Planned teaching and learning method
- Lecture with discussion
- Processing of exercises
- Interactive workshop
- Processing of exercises
- Interactive workshop
Semester/trimester in which the course/module is offered
1
Academic year
2
Key figure of the course/module
Type of course/module
Type of course
Internship(s)
-