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
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- State-of-the-art algorithms such as Support Vector Machines and Bayesian networks
- Key concepts, tools and approaches for pattern recognition on complex data sets
- Kernel methods for handling high dimensional and non-linear patterns
- Theoretical concepts and the motivations behind different learning frameworks
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
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 |
---|---|
Independent Study | 114 |
Teaching | 36 |
Total study time | 150 |
Resources & Reading list
Internet Resources
Reasoning and Machine Learning.
Information Theory, Inference, and Learning Algorithms.
Textbooks
John Shawe-Taylor and Nello Cristianini (2004). Kernel Methods for Pattern Analysis. Cambridge University Press.
Christopher M. Bishop (2006). Pattern Recognition and Machine Learning. Springer.
Aurelien Geron. Hands-On Machine Learning with Scikit-Learn & Tensor Flow.
Assessment
Summative
Summative assessment description
Method | Percentage contribution |
---|---|
Final Assessment | 80% |
Continuous Assessment | 20% |
Referral
Referral assessment description
Method | Percentage contribution |
---|---|
Set Task | 100% |
Repeat
Repeat assessment description
Method | Percentage contribution |
---|---|
Set Task | 100% |
Repeat Information
Repeat type: Internal & External