Machine Learning

Learning Outcomes

Transferable and Generic Skills

Having successfully completed this module you will be able to:

• Communicate the results of machine learning applications to specialized and non-specialized audiences.

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

• A broad set of machine learning techniques and their use in practice.

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

• Choose, compare and use appropriate machine learning techniques to address specific prediction/classification problems;
• Contrast the statistical modelling and machine learning approaches for the analysis of data;
• Assess the uncertainty associated to a given machine learning application using appropriate statistical measures;

• Introduction to the statistical modelling and machine learning approaches.
• Prediction for continuous responses: Linear regression, shrinkage
• Classification: Linear Discriminant Analysis (LDA), logistic regression, classification trees, Support Vector Machines (SVM)
• Dimensionality reduction: Principal Components Analysis (PCA)
• Clustering algorithms
• Ensemble methods: Bagging and Boosting
• Case studies

Teaching and learning methods

Depending on feasibility, teaching may be delivered face to face intensively over a week, or online using a mixture of synchronous and asynchronous online methods, which may include lectures, discussion boards, workshop activities, exercises, and videos. A range of resources will also be provided for further self-directed study.

Resources & Reading list

Journal Articles

Breiman, L. (2001). Statistical modeling: The two cultures. . Statistical science, 16(3), pp. 199-231.

Textbooks

Friedman, J., Hastie, T. and Tibshirani, R. (2017). The elements of statistical learning. . Springer.

James, G., Witten, D., Hastie, T. and Tibshirani, R. (2013). An introduction to statistical learning with applications in R.. Springer.

Assessment strategy

There will be opportunities to evaluate your progress through formative assessment, with summative assessment based on one online assignment.

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Repeat Information

Repeat type: Internal & External