Universität Wien

200222 SE Theorie und Empirie wissenschaftlichen Arbeitens (Geist und Gehirn) 1 (2024S)

8.00 ECTS (4.00 SWS), SPL 20 - Psychologie
Prüfungsimmanente Lehrveranstaltung

Dieses Seminar kann für alle Schwerpunkte absolviert werden!
Moodle

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

max. 20 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

TEWA 1: Computational Cognition

This course is intended to be a face-to-face course only.

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!

In the Mind and Brain track, we offer TEWA 1 and TEWA 2 courses, with TEWA 1 focusing more on computational aspects and theory, and TEWA 2 focusing on the practical use of specific data collection techniques. During your master's program, you are required to take one TEWA 1 and one TEWA 2.

  • Mittwoch 06.03. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Mittwoch 06.03. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Mittwoch 13.03. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Mittwoch 13.03. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Mittwoch 20.03. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Mittwoch 20.03. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Mittwoch 10.04. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Mittwoch 10.04. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Mittwoch 17.04. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Mittwoch 17.04. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Mittwoch 24.04. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Mittwoch 24.04. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Mittwoch 08.05. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Mittwoch 08.05. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Mittwoch 15.05. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Mittwoch 15.05. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Mittwoch 22.05. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Mittwoch 22.05. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Mittwoch 29.05. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Mittwoch 29.05. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Mittwoch 05.06. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Mittwoch 05.06. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Mittwoch 12.06. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Mittwoch 12.06. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Mittwoch 19.06. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Mittwoch 19.06. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Mittwoch 26.06. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Mittwoch 26.06. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

This 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 every Wednesday.

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.

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%

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: Do 25.01.2024 08:46