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| Semester | spring semester 2020 |
| Course frequency | Once only |
| Lecturers | Gerhard Lauer (gerhard.lauer@unibas.ch, Assessor) |
| Content | Machine Learning (ML) is a subset of artificial intelligence and a powerful tool to understand data, even predicting data. The seminar is an introduction to basic concepts and application of ML for research issues in the humanities and social sciences. |
| Learning objectives | The objective is to learn how to run machine learning for research in social sciences and humanities. Students will read and discuss basic papers in the field of ML in class, take part in hands-on sessions to practice ML, and write a research report in which they demonstrate their knowledge of ML. |
| Course application | Belegen |
| Language of instruction | English |
| Use of digital media | No specific media used |
| Interval | Weekday | Time | Room |
|---|
No dates available. Please contact the lecturer.
| Modules |
Modul: Digital Humanities, Culture and Society (Master's degree subject: Digital Humanities) |
| Assessment format | continuous assessment |
| Assessment details | Portfolio Students will write a research report in which they introduce a question of their choice, which they attempt to answer using machine learning. This report will be assessed as either a pass or a fail. |
| Assessment registration/deregistration | Reg.: course registration; dereg.: not required |
| Repeat examination | no repeat examination |
| Scale | Pass / Fail |
| Repeated registration | no repetition |
| Responsible faculty | Faculty of Humanities and Social Sciences, studadmin-philhist@unibas.ch |
| Offered by | Digital Humanities Lab |