Neural Networks (G5015)
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Neural Networks
Module G5015
Module details for 2024/25.
15 credits
FHEQ Level 6
Pre-Requisite
The course assumes an ability to write software in one appropriate programming language (e.g. Java, C, Python, Matlab). Basic knowledge of formal computational skills is also assumed.
Module Outline
Neural networks (NNs) are behind many of the most sophisticated and powerful artificial intelligence and machine learning tools used today. This module covers fundamental principles of NNs, different types of NN, methods to improve their performance, and their applications. Specific topics we’ll cover include:
• Loss functions for regression and classification
• Support vector machines
• NNs as universal function approximators
• Multi-layer perceptrons
• Convolutional NNs (CNNs)
• Recurrent NNs, including long-short-term-memory (LSTM)
• Advanced architectures and attention mechanisms
• Gradient descent, back-propagation, optimisers
• Regularisation, generalisation, gradient flow
• Encoding and feature learning
• Generative adversarial networks
• Deep reinforcement learning
• Graph neural networks
Library
1. Haykin S (1999). Neural networks. Prentice Hall International.
2. Bishop C (1995). Neural networks for pattern recognition. Oxford: Clarendon Press.
3. Duda RO, Hart PE and Stork DG (2001). Pattern Classification, John Wiley.
4. Ripley BD (1996). Pattern Recognition and Neural Networks. Cambridge University Press.
Module learning outcomes
refer to relevant mathematical concepts to describe how modern, deep neural networks can be used as universal function approximators.
describe and critique the principles and applications of different neural network architectures.
describe and critique the principles underlying different design considerations and techniques used to optimise the performance of neural networks.
apply their knowledge of neural networks by building, optimising, and analysing a neural network for a real-world problem.
| Type | Timing | Weighting |
|---|---|---|
| Coursework | 100.00% | |
| Coursework components. Weighted as shown below. | ||
| Problem Set | A2 Week 1 | 100.00% |
Timing
Submission deadlines may vary for different types of assignment/groups of students.
Weighting
Coursework components (if listed) total 100% of the overall coursework weighting value.
| Term | Method | Duration | Week pattern |
|---|---|---|---|
| Spring Semester | Lecture | 2 hours | 11111111111 |
| Spring Semester | Laboratory | 1 hour | 11111111111 |
How to read the week pattern
The numbers indicate the weeks of the term and how many events take place each week.
Dr James Bennett
Assess convenor
/profiles/415831
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