Universität Wien
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180148 VO+UE Tools in Cognitive Science I: Computation, Computer-aided Methodologies and Problem Solving (2020W)

5.00 ECTS (3.00 SWS), SPL 18 - Philosophie
Prüfungsimmanente Lehrveranstaltung
Moodle

An/Abmeldung

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

Details

max. 25 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

  • Freitag 02.10. 16:45 - 20:00 Digital
  • Freitag 09.10. 16:45 - 20:00 Digital
  • Freitag 16.10. 16:45 - 20:00 Digital
  • Freitag 23.10. 16:45 - 20:00 Digital
  • Freitag 06.11. 16:45 - 20:00 Digital
  • Freitag 13.11. 16:45 - 20:00 Digital
  • Freitag 20.11. 16:45 - 20:00 Digital
  • Freitag 27.11. 16:45 - 20:00 Digital
  • Freitag 04.12. 16:45 - 20:00 Digital
  • Freitag 11.12. 16:45 - 20:00 Digital
  • Freitag 18.12. 16:45 - 20:00 Digital
  • Freitag 08.01. 16:45 - 20:00 Digital
  • Freitag 15.01. 16:45 - 20:00 Digital
  • Freitag 22.01. 16:45 - 20:00 Digital
  • Freitag 29.01. 16:45 - 20:00 Digital

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

Different branches of computer science such as artificial intelligence, robotics, computational cognitive modelling, etc. can be considered core research fields of cognitive science. Analytical thinking and programming play fundamental roles in all these fields. Moreover, beyond their application in computer science, analytical thinking and programming are also widely used in other branches of cognitive science, such as the development of computer-based empirical experiments, data collection, data representation, data analysis, data visualisation, etc. Therefore, acquiring basic and working knowledge of analytical thinking and programming is an important learning step for all cognitive science students.

This course introduces students to:
• basic concepts of computation
• analytical, systemic and algorithmic thinking
• applied problem-solving skills
• working knowledge of programming (using Python), skills related to programming and application of programming in cognitive science
• practical skills related to design, implementation, and evaluation of computer-based empirical experiments, data collection, data representation, data analysis, data visualisation, etc.
• basic understanding of the role of computer science in cognitive science
• basic understanding of human-centric approaches in computer science as well as cognitive science
• basic understanding of ethics and accountability in computer science as well as cognitive science

____ COVID 19 Updates ____

Considering the current developments related to COVID-19, the course will be held fully online.

The decisions/updates, as well as detailed instructions will be announced via Moodle platform or email.

All communications, lectures, assignments, presentations, group-works, projects, evaluations, as well as exams will be done online.

– Course mode:
–– Distance mode (fully online)

– Teaching/learning methods:
– – Virtual synchronous course units,
– – Self-study with literature and online resources,
– – Virtual group-works,
– – Online exams,
– – Online presentations,
– – Virtual coaching/supervision sessions (if needed)
– – ...

– Students are expected to participate in announced online sessions, online presentations, online exams, etc.
– Students are expected to construct private virtual communication means for their virtual group works.
– Students are expected to regularly check their emails as well as the course Moodle environment.
– Students are expected to follow all detailed instructions related to remote teaching.
– The assessments will be based on the regular assessment plans (while everything will be done remotely).

Art der Leistungskontrolle und erlaubte Hilfsmittel

The course will be graded on a basis of 100 points in total:
• 100-87 points: Excellent (1)
• 86-75 points: Good (2)
• 74-63 points: Satisfactory (3)
• 62-50 points: Sufficient (4)
• 49-0 points: Unsatisfactory (5) (fail)

Mindestanforderungen und Beurteilungsmaßstab

Assessment criteria:
• 10% Active participation
• 25% Homework assignments (via Moodle)
• 20% Presentation (debriefing) of homework assignments
• 15% In-class quizzes
• 15% Final Exam
• 15% Final Project

• A positive score (>50%) in each of the above criteria is required for passing the course.
• Regular participation in at least 90% of sessions is obligatory.

Prüfungsstoff

Exam questions will be based on what we discuss in class, the readings and the homework assignments.

Literatur

• https://www.python.org/doc/
• Further readings will be announced in the course

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Fr 12.05.2023 00:18