CE889-7-AU-CO:
Neural Networks and Deep Learning
2023/24
Computer Science and Electronic Engineering (School of)
Colchester Campus
Autumn
Postgraduate: Level 7
Current
Thursday 05 October 2023
Friday 15 December 2023
15
21 February 2023
Requisites for this module
(none)
(none)
(none)
(none)
(none)
MSC G41112 Artificial Intelligence,
MSC G411CH Artificial Intelligence,
MSC H612CH Computer Engineering,
MSC G41212 Artificial Intelligence and its Applications
This module provides students with an understanding of artificial neural networks and deep neural networks in computer science and artificial intelligence.
The module assumes a reasonable Python programming background and is not suitable for students without prior Python programming experience.
The aim of this module is to provide students with an understanding of the role of artificial neural networks (ANNs) and deep neural networks in computer science and artificial intelligence.
After completing this module, students will be expected to be able to:
1. Demonstrate an understanding of the basic concepts and principles of neural computation as an approach to intelligent problem-solving.
2. Describe the commonly used neural network architectures and learning algorithms.
3. Distinguish classes of problems to which neural networks offer solutions superior to other methods.
4. Design a neural network to solve a particular problem.
5. Implement typical neural networks in software for regression and pattern classification.
6. Gain understanding and knowledge of deep neural networks.
The module assumes a reasonable Python programming background and is not suitable for students without prior Python programming experience.
Outline Syllabus
Introduction to Artificial Neural Networks
- basic concepts and principles of ANNs
- biological motivations and brief history of ANNs
- neuron models and neural network architectures
- computational power of ANNs in comparison with conventional AI methods
- ANN applications
Basic Learning Rules and Theories
- basic issues in neural network learning
- derivative-based methods such as error gradient descent learning algorithms
- derivative-free methods such as simulated annealing, genetic algorithm, Hebbian learning and competitive learning
- the bias-variance dilemma in learning from data
Feedforward Neural Networks Using Supervised Learning
- feedforward neural network architectures and supervised learning
- perceptron: architecture, error correction learning, limitations
- multilayer perceptron (MLP): architecture, back-propagation learning algorithm
- radial basis function (RBF) network: architecture, learning algorithm, comparison with MLP
Self-organising Neural Networks Using Unsupervised Learning
- unsupervised learning: learning without a teacher
- adaptive resonance theory (ART) neural network: architecture, learning algorithm
- self-organising map (SOM) neural network: architecture, learning algorithm
Recurrent Neural Networks
- recurrent neural network architectures
- Hopfield neural network: energy function, Hebbian learning, stability analysis
Deep Neural Networks
- concepts and architectures
ANN Applications and Recent Advances
- basic issues and strategies in neural network applications: data collection and preprocessing, classification, regression, prediction, and intelligent control
- recent advances in neural network research and development: support vector machine (SVM), reinforcement learning, neuro-fuzzy networks, etc.
Laboratories and Lectures
-
Goodfellow, I., Bengio, Y. and Courville, A. (2016)
Deep Learning. Cambridge, US: The MIT Press. Available at:
https://ebookcentral.proquest.com/lib/universityofessex-ebooks/detail.action?docID=6287197.
-
Nigrin, A. (1993)
Neural Networks for Pattern Recognition. Cambridge, US: MIT Press. Available at:
https://ebookcentral.proquest.com/lib/universityofessex-ebooks/detail.action?docID=5966120.
-
Thomas, S. and Passi, S. (2019)
PyTorch Deep Learning Hands-On: Build CNNs, RNNs, GANs, Reinforcement Learning, and More, Quickly and Easily. Birmingham, UK: Packt Publishing. Available at:
https://ebookcentral.proquest.com/lib/universityofessex-ebooks/detail.action?docID=5764289.
The above list is indicative of the essential reading for the course.
The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students.
Further reading can be obtained from this module's
reading list.
Assessment items, weightings and deadlines
| Coursework / exam |
Description |
Deadline |
Coursework weighting |
| Coursework |
Assignment 1 - Report on practical exercise |
|
100% |
| Exam |
Main exam: In-Person, Open Book (Restricted), 120 minutes during Early Exams
|
| Exam |
Reassessment Main exam: In-Person, Open Book (Restricted), 120 minutes during September (Reassessment Period)
|
Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Overall assessment
Reassessment
Module supervisor and teaching staff
Prof Hani Hagras, email: hani@essex.ac.uk.
Professor Hani Hagras
School Office, email: csee-schooloffice (non-Essex users should add @essex.ac.uk to create full e-mail address), Telephone 01206 872770
Yes
No
Yes
Dr Colin Johnson
University of Nottingham
Dr MARJORY CRISTIANY Da COSTA ABREU
Sheffield Hallam University
Senior Lecturer
Available via Moodle
Of 80 hours, 20 (25%) hours available to students:
60 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s), module, or event type.
Disclaimer: The University makes every effort to ensure that this information on its Module Directory is accurate and up-to-date. Exceptionally it can
be necessary to make changes, for example to programmes, modules, facilities or fees. Examples of such reasons might include a change of law or regulatory requirements,
industrial action, lack of demand, departure of key personnel, change in government policy, or withdrawal/reduction of funding. Changes to modules may for example consist
of variations to the content and method of delivery or assessment of modules and other services, to discontinue modules and other services and to merge or combine modules.
The University will endeavour to keep such changes to a minimum, and will also keep students informed appropriately by updating our programme specifications and module directory.
The full Procedures, Rules and Regulations of the University governing how it operates are set out in the Charter, Statutes and Ordinances and in the University Regulations, Policy and Procedures.