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Vorlesungsverzeichnis >> Fakultät Humanwissenschaften >>
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Machine Learning for Psychology and Social Sciences (ML for Psychology)- Dozent/in
- Dr. Alexander Pastukhov
- Angaben
- Seminar
Präsenz + Online-Anteile
2 SWS
Zeit und Ort: Do 10:00 - 12:00, M3N/-1.19
- Voraussetzungen / Organisatorisches
- Basic knowledge of Python or R would be helpful.
- Inhalt
- Psychology and social sciences rely statistical analysis to infer and describe causal effects that guide our behavior and determine our actions and responses. Proliferation of computers and smartphones, as well as a widespread access to internet made collection large datasets possible. In addition, movement towards open data in science means that there are many data sets both in panel databanks such as NEPS, SOEP, or ZPID, and at online repositories such as OSF or GitHub that can be analyzed and used to guide research and experimental design. However, the sheer amount of data makes using classic statistical methods cumbersome. The aim of the seminar is show how modern machine learning methods can be used to prescreen and analyze such big data sets. It will cover both the basic theory of methodology (math will be used very sparingly) and practical use of methods in Python and R (both free and open source systems). The material will cover a broad variety of supervised and unsupervised machine learning methods, including linear and logistic regression (which should be familiar from statistics), support-vector machines, tree-based approaches, cluster analysis, modeless analysis via nearest neighbor, deep neural networks, etc. A particular focus will be on application of these methods to "typical" social sciences / psychology data.
- Empfohlene Literatur
- Hands-On Machine Learning with Scikit-Learn and TensorFlow
by Aurélien Géron
ISBN: 9781491962299
- Englischsprachige Informationen:
- Title:
- Machine Learning for Psychology and Social Sciences
- Credits: 3
- Zusätzliche Informationen
- Erwartete Teilnehmerzahl: 12
- Institution: Lehrstuhl für Allgemeine Psychologie und Methodenlehre
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