200222 SE Theorie und Empirie wissenschaftlichen Arbeitens (Geist und Gehirn) 1 (2025S)
Prüfungsimmanente Lehrveranstaltung
Labels
Dieses TEWA 1 kann für alle Schwerpunkte absolviert werden.
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mo 03.02.2025 09:00 bis Do 27.02.2025 09:00
- Abmeldung bis Mo 03.03.2025 09:00
Details
max. 20 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
- Dienstag 04.03. 16:45 - 20:00 Hörsaal H Psychologie KG Liebiggasse 5
- Dienstag 11.03. 16:45 - 20:00 Hörsaal H Psychologie KG Liebiggasse 5
- Dienstag 18.03. 16:45 - 20:00 Hörsaal H Psychologie KG Liebiggasse 5
- Dienstag 25.03. 16:45 - 20:00 Hörsaal H Psychologie KG Liebiggasse 5
- Dienstag 01.04. 16:45 - 20:00 Hörsaal H Psychologie KG Liebiggasse 5
- Dienstag 08.04. 16:45 - 20:00 Hörsaal H Psychologie KG Liebiggasse 5
- Dienstag 29.04. 16:45 - 20:00 Hörsaal H Psychologie KG Liebiggasse 5
- Dienstag 06.05. 16:45 - 20:00 Hörsaal H Psychologie KG Liebiggasse 5
- Dienstag 13.05. 16:45 - 20:00 Hörsaal H Psychologie KG Liebiggasse 5
- N Dienstag 20.05. 16:45 - 20:00 Hörsaal H Psychologie KG Liebiggasse 5
- Dienstag 27.05. 16:45 - 20:00 Hörsaal H Psychologie KG Liebiggasse 5
- Dienstag 03.06. 16:45 - 20:00 Hörsaal H Psychologie KG Liebiggasse 5
- Dienstag 10.06. 16:45 - 20:00 Hörsaal H Psychologie KG Liebiggasse 5
- Dienstag 17.06. 16:45 - 20:00 Hörsaal H Psychologie KG Liebiggasse 5
- Dienstag 24.06. 16:45 - 20:00 Hörsaal H Psychologie KG Liebiggasse 5
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
TEWA 1: Computational CognitionThis course provides an introduction to the leading computational frameworks for understanding human behavior and cognition. Psychologists are increasingly confronted with large amounts of human behavioral data, and computational methods can help make sense of this data. The course focuses on practical and programming aspects, while also discussing the theoretical implications for psychology and cognitive science. To this end, the course will consist of conceptual lectures for the first 90 minutes, followed by programming labs for another 90 minutes.The course will be divided into three parts. The first part will introduce the Python programming language and the most important libraries for data analysis, including NumPy, SciPy, Matplotlib, and pandas. The second part will focus on general data science methods, such as statistical inference with resampling methods, regression models, and machine learning. In the final section of the course, we will apply the programming skills we have acquired to model concepts that are particularly relevant to cognitive science, such as neural networks and reinforcement learning.Weekly homework assignments will involve testing and implementing the techniques taught in the current and possibly previous weeks. The final project will require students to work in groups on a specific topic to analyze data, write a proposal, and present the project in class. By the end of the course, students will have a more comprehensive understanding of how computational methods advance psychology, how psychology can inform research in machine learning and AI, and how cognitive models are adapted and evaluated to understand behavioral data.If you have no programming background, it is recommended that you spend some hours on introductory Python material (e.g. www.learnpython.org, www.datacamp.com) before the course starts. This will make the first few weeks of the course much easier!Please bring a laptop.
Art der Leistungskontrolle und erlaubte Hilfsmittel
Homework assignments will be announced via Moodle and should be uploaded to Moodle by the due date.The topics for the final project will be determined in May and students will work on the final project in the final weeks of the course. Full details, including deadlines, will be announced on Moodle.
Mindestanforderungen und Beurteilungsmaßstab
Weekly individual assignments (weighted with 50%, must have an average grade of at least 60% to pass the course)
Individual in-class evaluations (15%, maximum 2 class absences without an excuse)
Group final project (35%, all group members receive the same grade, must have a grade of at least 60% to pass the course)The overall grade will be a weighted average of the above:1: ≥ 90%
2: ≥ 80%
3: ≥ 70%
4: ≥ 60%
5: < 60%
Individual in-class evaluations (15%, maximum 2 class absences without an excuse)
Group final project (35%, all group members receive the same grade, must have a grade of at least 60% to pass the course)The overall grade will be a weighted average of the above:1: ≥ 90%
2: ≥ 80%
3: ≥ 70%
4: ≥ 60%
5: < 60%
Prüfungsstoff
There is no final exam, but there will be homework and a final project. All content covered in the course will be relevant. Supporting materials will be available on Moodle.
Literatur
Will be announced on Moodle.
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Di 25.02.2025 13:27