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Machine Learning

This module provides a broad view of machine learning and statistical pattern recognition.

You will learn several techniques, including supervised learning (e.g. generative and discriminative learning, parametric and non-parametric learning), unsupervised learning (e.g. clustering), and theoretical aspects of machine learning (e.g. bias, variance).

The module will also discuss recent applications of machine learning to areas of interest to data scientists.

Topics covered

  • Introduction to Machine Learning
  • Classification
  • Regression
  • Model Improvement
  • Unsupervised Learning
  • Ensemble Methods
  • Neural Networks and Deep Learning
  • Working with Timeseries
  • Probabilistic Modelling
  • Ethics and Sustainability.


15 (150 hours)
Summative coursework (30%)


  • Written examination (70%)