Postgraduate Course: Seminar in Cognitive Modelling (UG) (INFR11237)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course follows the delivery and assessment of Seminar in Cognitive Modelling (INFR11210) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11210 instead. |
| Course description |
This course follows the delivery and assessment of Seminar in Cognitive Modelling (INFR11210) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11210 instead.
|
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
It is RECOMMENDED that students have passed
Computational Cognitive Science (INFR10054) OR
Computational Cognitive Neuroscience (UG) (INFR11233)
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Co-requisites | |
| Prohibited Combinations | Students MUST NOT also be taking
Seminar in Cognitive Modelling (INFR11210) AND
Informatics Research Review (INFR11136)
|
Other requirements | This course follows the delivery and assessment of Seminar in Cognitive Modelling (INFR11210) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11210 instead.
This course is only open to students in Informatics and PPLS whose DPT lists this course. PPLS students and PTs should take note of the "other requirements" if they cannot take the recommended co-requisite.
The course assumes knowledge of cognitive science and, by the second semester, knowledge of linear algebra (vectors / matrix multiplication, orthogonality, eigenvectors), probability theory (discrete and continuous univariate random variables, expectations, Bayes rule), statistics (linear / logistic regression) and model evaluation.
Data visualization and programming experience will be useful but there is no required programming. |
Information for Visiting Students
| Pre-requisites | As above. |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2024/25, Not available to visiting students (SS1)
|
Quota: 5 |
| Course Start |
Full Year |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 6,
Seminar/Tutorial Hours 27,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
163 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Weekly brief («200 words) engagement responses to readings and in-class discussions (30%)
Essay in first semester (40%)
Oral presentation in the second semester (30%) |
| Feedback |
Written feedback on essays and portfolio.
Verbal feedback on presentations. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- demonstrate understanding of a range of classic and current articles in cognitive science / modelling by summarizing and critiquing their central ideas and / or results
- demonstrate understanding of the relationship between computational models and cognitive theories, by being able to critically assess the theoretical adequacy of a given model
- compare and contrast the strengths and weaknesses of different models of the same behaviour
- search the literature and synthesize information from several papers on the same topic and create a coherent oral presentation on that topic
- communicate (written and oral) key findings in cognitive science/modelling to inter-disciplinary audiences
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Additional Information
| Course URL |
https://opencourse.inf.ed.ac.uk/scm |
| Graduate Attributes and Skills |
Critical / analytical thinking, knowledge integration and application, independent learning, creativity, interpersonal skills, verbal, written and cross-disciplinary communication |
| Keywords | SCM,cognitive science,cognitive modelling,science communication |
Contacts
| Course organiser | Dr Maithilee Kunda
Tel:
Email: mkunda@ed.ac.uk |
Course secretary | Ms Lindsay Seal
Tel: (0131 6)50 5194
Email: lindsay.seal@ed.ac.uk |
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