Machine Learning

Learning Outcomes

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

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

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.

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.

• 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

The course will include lectures and practical sessions in R, mixed in a 5 day course designed for students on release from the workplace. Students are also expected to read wider than the lecture material as part of their individual study, and to critically appraise different approaches.

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

The course will be assessed by a written report representing 100% of the marks.

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