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Instructors: Kyra Ahrens; Dr. Annika Peters
Event type:
Seminar
Displayed in timetable as:
RM - Sem
Hours per week:
2
Credits:
3,0
Language of instruction:
English
Min. | Max. participants:
- | 25
Comments/contents:
This practical seminar complements the lecture. For information on content and aims, please refer to the lecture description.
Learning objectives:
The objective of this practical seminar is to gain a deeper understanding of the concepts taught in the lecture. Successful students will be able to apply the theoretical concepts of the lecture and are able to design meaningful experiments to answer specific hypothesis, know about different types of data and methods to analyse them statistically, have learned how to defend their approach in scientific discussion and publication, and can critically evaluate scientific literature. By implementing the methods taught in the lecture in Python, students get hands-on experience and examples and are able to use those on a variety of problems.
Didactic concept:
The practical seminar will be tightly interwoven with the lecture and will include the following:
- designing and executing own experiments and discuss design, execution and outcome,
- analysing and explore gathered and given data with different methods/tools,
- tailoring experiment designs to answer specific questions and test hypothesis,
- discussing scientific literature and critically evaluate the claims and results,
- implement methods in Python for data analysis and visualisation.
The main focus within these activities will be on mimicking a realistic scientific environment and process. Students will work partly independent in groups of varying sizes and meet on a regular basis to discuss and defend their work with a tutor.
The seminar 20/21 will be held online over video chat and collaboration tools due to changes because of the Corona virus. The exact procedure and implementation will be discussed with the students at the first meeting.
Literature:
• Cohen, P. R. Empirical methods for artificial intelligence. MIT Press, Cambridge, Mass. 1995.
• Field, A., Miles, J., Field, Z. Discovering statistics using R. SAGE, Los Angeles, 2012.
• Allen B. Downey., Think Stats 2e. Green Tea Press Books, 2014. (Freely available online)
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