Universität Wien

180148 VO+UE Tools in Cognitive Science I: From computation and programming to human centric digital sciences (2022W)

5.00 ECTS (3.00 SWS), SPL 18 - Philosophie
Continuous assessment of course work
ON-SITE

Preparation meeting: Monday October 3rd, 2022, 9:00
HS 2i, NIG, Universitätsstrasse 7, 2nd floor
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 25 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

You are expected to bring your laptop to the course.

  • Thursday 06.10. 09:45 - 11:15 Hörsaal 3B NIG 3.Stock
  • Thursday 13.10. 09:45 - 13:00 Hörsaal 3B NIG 3.Stock
  • Thursday 20.10. 09:45 - 13:00 Hörsaal 3B NIG 3.Stock
  • Thursday 27.10. 09:45 - 13:00 Hörsaal 3B NIG 3.Stock
  • Thursday 03.11. 09:45 - 13:00 Hörsaal 3B NIG 3.Stock
  • Thursday 10.11. 09:45 - 13:00 Hörsaal 3B NIG 3.Stock
  • Thursday 17.11. 09:45 - 13:00 Hörsaal 3B NIG 3.Stock
  • Thursday 24.11. 09:45 - 13:00 Hörsaal 3B NIG 3.Stock
  • Thursday 01.12. 09:45 - 13:00 Hörsaal 3B NIG 3.Stock
  • Thursday 15.12. 09:45 - 13:00 Hörsaal 3B NIG 3.Stock
  • Thursday 12.01. 09:45 - 13:00 Hörsaal 3B NIG 3.Stock
  • Thursday 19.01. 09:45 - 13:00 Hörsaal 3B NIG 3.Stock
  • Thursday 26.01. 09:45 - 13:00 Hörsaal 3B NIG 3.Stock

Information

Aims, contents and method of the course

Different branches of digital sciences and computer science such as artificial intelligence, robotics, computational cognitive modelling, human-computer interaction, digital ethics, human-centric computing, etc., can be considered important (and even core) research fields of cognitive science. Analytical thinking and programming play fundamental roles in all these fields. Moreover, beyond their application in digital sciences and 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 essential for all cognitive science students.

This course introduces students to:
• basic concepts and challenges of digital sciences
• 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, representation, analysis, 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 partially (or entirely) online (depending on the situation).

The decisions/updates and detailed instructions will be announced via Moodle platform or email.

Most of (or maybe all of) the communications, lectures, assignments, presentations, group-works, projects, evaluations, and exams will be done online.

– Course mode:
–– Mixed/hybrid (online + in-person) or fully online

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

– Students are expected to participate in announced online/in-person sessions, online/in-person presentations, online/in-person 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/in-person teaching.
– The assessments will be based on the regular assessment plans.

Assessment and permitted materials

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)

Minimum requirements and assessment criteria

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.

No materials or tools may be used or drawn upon during the quizzes and final exam

Examination topics

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

Reading list

- Beecher, K. (2017). Computational Thinking: A beginner’s guide to problem-solving and programming (Illustrated edition). BCS, The Chartered Institute for IT.
- Bouras, A. S. (2019). Python and Algorithmic Thinking for the Complete Beginner (2nd Edition): Learn to Think Like a Programmer. Independently published.
- Farrell, S. (2018). Computational Modeling of Cognition and Behavior. Cambridge University Press.
- Curzon, P., & Mcowan, P. W. (2017). Power Of Computational Thinking, The: Games, Magic And Puzzles To Help You Become A Computational Thinker. WSPC.
- Jesús, S. D., & Martinez, D. (2020). Applied Computational Thinking with Python: Design algorithmic solutions for complex and challenging real-world problems. Packt Publishing.
- Maeda, J. (2019). How to Speak Machine: Computational Thinking for the Rest of Us. Portfolio.
- McKinney, W. (2017). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython (2nd edition). O’Reilly UK Ltd.
- Miller, B. N., & Ranum, D. L. (2011). Problem Solving with Algorithms and Data Structures Using Python (002 edition). FRANKLIN BEEDLE & ASSOC.
- Zingaro, D. (2020). Algorithmic Thinking: A Problem-Based Introduction. No Starch Press.
- Géron, A. (2019). Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (2nd ed edition). O’Reilly UK Ltd.

Further literature will be announced in the course

Association in the course directory

Last modified: Th 06.10.2022 14:09