Module overview
Machine Learning is about extracting useful information from large and complex datasets. The module will cover the practical basis of how learning algorithms are can be applied. You will gain hands-on experience in laboratory-bases sessions.
Exclusions: Cannot be taken with COMP3206 or COMP3222 or COMP3223 or COMP6229 or COMP6245.
Aims and Objectives
Learning Outcomes
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Critically appraise the uses of Deep Learning
- Appreciation of the landscape of tools used for modern machine learning
- Characterise data in terms of explanatory models
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- Implementation issues in learning algorithms and the evaluation of their performance.
- The algorithmic basis of machine learning algorithms
Subject Specific Practical Skills
Having successfully completed this module you will be able to:
- Systematically work with data to learn new patterns or concepts
- Gain expertise in working with machine learning to make predictions in a scientific computing environment
Syllabus
Historical Perspective
- Biological motivations: the McCulloch and Pitts neuron, Hebbian learning.
- Conceptual motivations
Tools in machine learning
- Libraries
- Implementing and evaluating algorithms
Supervised Learning
- Perceptron Algorithm
- Support Vector Classification and Regression
- Neural networks/multi-layer perceptrons (MLP)
Data handling and unsupervised learning
- Principal Components Analysis (PCA)
- K-Means clustering
Regression and Model-fitting Techniques
- Linear regression
Deep Learning
- Deep Neural Networks (CNN, RNN)
Case Studies
- Example applications: Speech, Vision, Natural Language, Bioinformatics.
Learning and Teaching
Teaching and learning methods
Lectures, labs and guided self-study
| Type | Hours |
|---|---|
| Specialist Laboratory | 20 |
| Wider reading or practice | 21 |
| Revision | 10 |
| Lecture | 24 |
| Completion of assessment task | 75 |
| Total study time | 150 |
Resources & Reading list
Textbooks
Aurélien Géron. Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems.
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Coursework | 50% |
| Examination | 50% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Examination | 100% |
Repeat
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
| Method | Percentage contribution |
|---|---|
| Examination | 100% |
Repeat Information
Repeat type: Internal & External