THE UNIVERSITY of EDINBURGH

DEGREE REGULATIONS & PROGRAMMES OF STUDY 2020/2021

Information in the Degree Programme Tables may still be subject to change in response to Covid-19

University Homepage
DRPS Homepage
DRPS Search
DRPS Contact
DRPS : Course Catalogue : School of Philosophy, Psychology and Language Sciences : Psychology

Undergraduate Course: Introduction to Neural Network Modelling (PSYL10151)

Course Outline
SchoolSchool of Philosophy, Psychology and Language Sciences CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 10 (Year 3 Undergraduate) AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
SummaryThis 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.
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.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Research Methods and Statistics (PPLS08001)
Co-requisites
Prohibited Combinations Other requirements Students must have completed RMS or equivalent and have some basic programming knowledge.
Information for Visiting Students
Pre-requisitesNone
High Demand Course? Yes
Course Delivery Information
Not being delivered
Learning Outcomes
On completion of this course, the student will be able to:
  1. Understanding of basic feed-forward, recurrent, and auto-associative neural networks.
  2. Understanding of basic supervised and unsupervised methods for training neural networks.
  3. Appreciation of the scope of neural networks as tools for understanding cognition.
  4. Appreciation of the scope of neural networks as tools for solving computational problems.
  5. Understanding of the associations between the basic properties of neural networks, and our current understanding of real neural systems.
Reading List
None
Additional Information
Graduate Attributes and Skills Not entered
KeywordsPsychology
Contacts
Course organiserDr Alex Doumas
Tel: (0131 6)51 1328
Email: Alex.Doumas@ed.ac.uk
Course secretaryMs Alex MacAndrew
Tel: (0131 6)51 3733
Email: alexandra.macandrew@ed.ac.uk
Navigation
Help & Information
Home
Introduction
Glossary
Search DPTs and Courses
Regulations
Regulations
Degree Programmes
Introduction
Browse DPTs
Courses
Introduction
Humanities and Social Science
Science and Engineering
Medicine and Veterinary Medicine
Other Information
Combined Course Timetable
Prospectuses
Important Information