GGS-Workshop: "Introduction to Machine Learning"
- https://www.uni-giessen.de/de/fbz/zentren/ggs/veranstaltungen/index_html/wise24_25/machine_learning_2024
- GGS-Workshop: "Introduction to Machine Learning"
- 2024-11-25T09:00:00+01:00
- 2024-11-28T16:00:00+01:00
25.11.2024 09:00 bis 28.11.2024 16:00 (Europe/Berlin / UTC100)
Room 308, Otto-Behaghel-Straße 12, 35394 Giessen
Doctoral candidates or postdoctoral researchers doing empirical research or intending to work as data scientists
| Instructor: | Dr Tobias Keller | |
|
Dates: |
November 25, 26, 27, and 28, 2024, 9.00 am - 4.00 pm |
|
| Max. participants: | 12 | |
| Course language: | English (German, if only German participants) | |
| Registration Deadline: | November 15, 2024 | |
| ECTS: | 4 |
Objectives
After this workshop, participants understand the basic concepts and typical process of machine learning and how it enables artificial intelligence systems. On the basis of examples from practice and research, participants will learn to think like data scientists, and to ask the right questions. They will also learn to apply and evaluate algorithms for supervised and unsupervised machine learning tasks using the open source software KNIME, which does not require programming knowledge. Since there will be no need to learn a programming language during the course, more time will be allocated to understanding the algorithms on an intuitive, non-formal level.
Content and methods
The course consists of lectures to build the theoretical background as well as hands-on tutorials and exercises using the machine learning software KNIME. Participants will learn by examples and exercises from the instructor’s experience in research and practice. Hands-on tutorials and exercises will make up about 40 per cent of the course time. Students will complete those using their own computers. Please see the preparation requirements below for a list
of software that needs to be installed to that end. The exercises will be based on exemplary datasets that will be provided to the participants before the course.
You can find more details in the syllabus "Introduction to Machine Learning".