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

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

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.

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.