CS985 – Machine Learning for Data Analytics

Credits 20
Level 5
Semester 2
Prerequisites N/A
Availability Possible elective
Contact Lectures: 20 | Labs: 20
Homework / Private Study: 160
Assessment Two individual assignments worth 50%, and two class tests (worth 50%).
Resit TBC
Lecturer Dr Marc Roper | Dr Ross Duncan

General Aims

The aim of this class is to equip students with a sound understanding of the principles of machine learning and a range of popular approaches, along with the knowledge of how and when to apply the techniques.

Learning Outcomes

After completing this class participants will be able to:

  • understand the aims and fundamental principles of machine learning;
  • understand a range of the key algorithms and approaches to machine learning;
  • apply the algorithms covered and interpret the outcomes;
  • understand the applicability of the algorithms to different types of data and problems along with their strengths and limitations.


Part 1: Fundamentals

  • Machine learning basics
  • Supervised vs unsupervised learning
  • Supervised learning strategies
    • Regression
    • Linear regression, decision trees, Bayesian networks
    • Classification
    • Logistic regression, classification trees
  • Unsupervised learning strategies
    • Clustering techniques
    • k-means, hierarchical, Gaussian mixture models
    • Dimension reduction (PCA)

Part 2: Advanced topics

  • Semi-Supervised learning (EM etc.)
  • Random Forests
  • Support Vector Machines
  • Artificial Neural Networks
  • Genetic Algorithms

Recommended Text/Reading

The class lecturers will recommend a mixture of specific and generic references for each part of the course. Where possible, free online material will be recommended.