CE889-7-AU-CO:
Neural Networks and Deep Learning

The details
2024/25
Computer Science and Electronic Engineering (School of)
Colchester Campus
Autumn
Postgraduate: Level 7
Current
Thursday 03 October 2024
Friday 13 December 2024
15
21 February 2023

 

Requisites for this module
(none)
(none)
(none)
(none)

 

(none)

Key module for

MSC G41112 Artificial Intelligence,
MSC G411CH Artificial Intelligence,
MSC H612CH Computer Engineering,
MSC G456N2 Computing

Module description

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.

Module aims

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.

Module learning outcomes

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.

Module information

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.

Learning and teaching methods

Laboratories and Lectures

Bibliography

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   13/12/2024  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

Coursework Exam
20% 80%

Reassessment

Coursework Exam
20% 80%
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

 

Availability
No
No
Yes

External examiner

Dr Colin Johnson
University of Nottingham
Dr MARJORY CRISTIANY Da COSTA ABREU
Sheffield Hallam University
Senior Lecturer
Resources
Available via Moodle
Of 24 hours, 22 (91.7%) hours available to students:
2 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s), module, or event type.

 

Further information

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