Undergraduate Course: Introduction to Neural Network Modelling (PSYL10151)
Course Outline
| School | School of Philosophy, Psychology and Language Sciences |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 10 (Year 3 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course provides an introduction to neural networks and their use in understanding human and non-human animal cognition. In specific, students will be exposed to simple auto-associative, feed-forward, and recurrent network architectures, and Hebbian, back-propagation, and unsupervised training methods. Students will also be exposed to more recent developments in deep neural networks. The course emphasizes the use of neural networks as tools for understanding cognition and for instantiating cognitive theories.
Students will receive a crash course in the Python programming language. They will use Python to develop simple neural networks. Students will also be exposed to the Keras library for building more complex neural networks. They will use Keras to develop a larger network project.
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| Course description |
This course provides an introduction to neural networks and their use in understanding human and non-human animal cognition. In specific, students will be exposed to simple auto-associative, feed-forward, and recurrent network architectures, and Hebbian, back-propagation, and unsupervised training methods. Students will also be exposed to more recent developments in deep neural networks. The course emphasizes the use of neural networks as tools for understanding cognition and for instantiating cognitive theories.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
Students MUST have passed:
Research Methods and Statistics (PPLS08001)
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | Students must have completed RMS or equivalent and have some basic programming knowledge. |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
| Not being delivered |
Learning Outcomes
On completion of this course, the student will be able to:
- Understanding of basic feed-forward, recurrent, and auto-associative neural networks.
- Understanding of basic supervised and unsupervised methods for training neural networks.
- Appreciation of the scope of neural networks as tools for understanding cognition.
- Appreciation of the scope of neural networks as tools for solving computational problems.
- Understanding of the associations between the basic properties of neural networks, and our current understanding of real neural systems.
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Psychology |
Contacts
| Course organiser | Dr Alex Doumas
Tel: (0131 6)51 1328
Email: Alex.Doumas@ed.ac.uk |
Course secretary | Ms Alex MacAndrew
Tel: (0131 6)51 3733
Email: alexandra.macandrew@ed.ac.uk |
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