Undergraduate Course: Computational Foundations of Cognitive Science 1 (INFR08011)
Course Outline
| School |
School of Informatics |
College |
College of Science and Engineering |
| Course type |
Standard |
Availability |
Available to all students |
| Credit level (Normal year taken) |
SCQF Level 8 (Year 1 Undergraduate) |
Credits |
20 |
| Home subject area |
Informatics |
Other subject area |
None |
| Course website |
http://www.inf.ed.ac.uk/teaching/courses/cfcs1
|
Taught in Gaelic? |
No |
| Course description |
This course is designed to teach students the computational and mathematical approaches that form the quantitative foundation of cognitive science. The mathematical content of the course includes basic linear algebra and and an introduction to probability and information theory. All mathematical and computational content is supported by putting it in the context of a cognitive science application. Computational tools, such as Matlab, will play an important role in the presentation of the course. |
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites |
|
| Prohibited Combinations |
|
Other requirements |
None
|
| Additional Costs |
None |
Information for Visiting Students
| Pre-requisites |
None |
| Displayed in Visiting Students Prospectus? |
No |
Course Delivery Information
| Not being delivered |
Summary of Intended Learning Outcomes
1 - Demonstrate knowledge of the mathematical concepts and techniques covered in this course by being able to apply them to relevant mathematical problems.
2 - Recognise the cognitive relevance of the mathematical concepts and techniques covered in this course by being able to apply them to simple modelling problems and applications in cognitive science.
3 - Describe the relationship between the mathematical formalisation of a cognitive system and its biological implementation and assess the adequacy of the formalisation.
4 - Demonstrate an understanding of usage of computational tools to apply mathematical concepts to problems in cognitive science.
5 - Analyse and evaluate scientific arguments and present the results of the evaluation in written or oral form. |
Assessment Information
Written Examination 75
Assessed Assignments 25
Oral Presentations 0
Assessment
The assessed coursework consists of four assignments which cover material from the tutorials and the lab sessions. They carry equal weight, i.e., each assignment is worth 6.25% of the overall mark for the course.
If delivered in semester 1, this course will have an option for semester 1 only visiting undergraduate students, providing assessment prior to the end of the calendar year. |
Special Arrangements
| None |
Additional Information
| Academic description |
Not entered |
| Syllabus |
The syllabus is in four parts: introduction to Matlab, linear algebra, probability and information theory, and cognitive science applications. Matlab and cognitive science applications will be interleaved within the other parts, and the links between the mathematical foundation and the cognitive application will be shown.
1. Introduction to Matlab
* Overview
* Programming
* Vectors
* Matrices
* Plotting and graphics
2. Linear Algebra
* Vectors, vector operations
* Distances, norm, dot product
* Basic operations on matrices; matrix product
* Algebraic properties of matrices; transpose; inner and outer product
* Inverses; matrices with special forms
* Determinants and eigenvectors
* Convolutions and kernels
3. Probability and Information Theory
* Introduction to probability theory, combinatorial methods
* Sample spaces, events, probabilities
* Conditional probability, Bayes' theorem
* Discrete and continuous random variables; distributions and densities
* Special distributions and densities
* Joint, marginal, and conditional distributions
* Expectation and variance
* Entropy, Kullback-Leibler divergence
* Codes
* Minimum discription length
4. Cognitive Science Applications
* Models of learning
* Models of semantic processing
* Models of object recognition
* Models of reasoning
* Models of eyetracking data
* Language models
Relevant QAA Computing Curriculum Sections: Artificial Intelligence |
| Transferable skills |
Not entered |
| Reading list |
* Anton, Howard and Robert C. Busby. 2003. Contemporary Linear Algebra. John Wiley, New York.
* Cover, Thomas M. and Joy A. Thomas. 2006. Elements of Information Theory. 2nd edition. John Wiley, New York.
* McMahon, David. 2007. MATLAB Demystified. McGraw-Hill, New York.
* Miller, Irwin and Marylees Miller. 2004. John E. Freund's Mathematical Statistics with Applications. 7th edition. Pearson Education, London. |
| Study Abroad |
Not entered |
| Study Pattern |
Lectures 30
Tutorials 8
Timetabled Laboratories 20
Non-timetabled assessed assignments 40
Private Study/Other 102
Total 200 |
| Keywords |
Not entered |
Contacts
| Course organiser |
Dr Miles Osborne
Tel: (0131 6)50 4430
Email: osborne@cogsci.ed.ac.uk |
Course secretary |
Ms Kirsten Belk
Tel: (0131 6)50 5194
Email: kbelk@staffmail.ed.ac.uk |
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