CS986 – Fundamentals of Machine Learning for Data Analytics

TIMETABLE TEACHING MATERIAL
Credits 10
Level 5
Semester 2
Prerequisites N/A
Availability Possible elective
Contact Lectures: 10 | Labs: 10
Homework / Private Self study: 80
Assessment One individual assignment worth 50%. One class test worth 50%.
Resit TBC
Lecturer Dr Marc Roper

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 essential core 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.

Syllabus

  • 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)

Recommended Text/Reading*

The class lecturer will recommend a mixture of specific and generic references. Where possible, free online material will be recommended.