Course overview
A practical introduction to data modelling, analysis and prediction using contemporary software packages. An overview of common techniques and their implementation in software libraries. Selection of tools and techniques that are appropriate for different types and scale of data. Validation and interpretation of process outputs.
Course learning outcomes
- Generate unsupervised and supervised methods to gain Information to bring about solutions
- Formulate a range of regression methods as part of supervised learning
- Formulate a range of classification methods as part of supervised learning
- Construct tree methods to map non-linear relationships as part of supervised learning
- Investigate the role of machine learning method in support of all the other techniques