Module overview
- To introduce key concepts in pattern recognition and machine learning; including specific algorithms for classification, regression, clustering and probabilistic modeling.
- To give a broad view of the general issues arising in the application of algorithms to analysing data, common terms used, and common errors made if applied incorrectly.
- To demonstrate a toolbox of techniques that can be immediately applied to real world problems, or used as a basis for future research into the topic.
Linked modules
Pre-requisites: COMP3206 or COMP3223 or COMP6229 or COMP6245
Aims and Objectives
Learning Outcomes
Subject Specific Practical Skills
Having successfully completed this module you will be able to:
- Be able to solve real-world machine learning tasks: from data to inference
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Conceptually understand the role of pattern analysis and probabilistic modeling, together with the mathematical techniques this requires
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- Key concepts, tools and approaches for pattern recognition on complex data sets
- State-of-the-art algorithms such as Support Vector Machines and Bayesian networks
- Kernel methods for handling high dimensional and non-linear patterns
- Theoretical concepts and the motivations behind different learning frameworks
Syllabus
Key concepts
- Supervised/Unsupervised Learning
- Loss functions and generalization
- Probability Theory
- Elements of Computational Learning Theory
Kernel Methods for non-linear data
- Support Vector Machines
- Kernels
Bayesian methods for using prior knowledge and data
- Bayesian inference
- Bayesian Belief Networks and Graphical models
- Gaussian Processes
Ensemble Learning
- Bagging
- Boosting
- Random Forest
Learning and Teaching
| Type | Hours |
|---|---|
| Teaching | 36 |
| Independent Study | 114 |
| Total study time | 150 |
Resources & Reading list
Internet Resources
Information Theory, Inference, and Learning Algorithms.
Reasoning and Machine Learning.
Textbooks
Christopher M. Bishop (2006). Pattern Recognition and Machine Learning. Springer.
Aurelien Geron. Hands-On Machine Learning with Scikit-Learn & Tensor Flow.
John Shawe-Taylor and Nello Cristianini (2004). Kernel Methods for Pattern Analysis. Cambridge University Press.
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Final Assessment | 80% |
| Continuous Assessment | 20% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Set Task | 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 |
|---|---|
| Set Task | 100% |
Repeat Information
Repeat type: Internal & External