Foundations of Machine Learning

Unit code: COMP61011
Credit Rating: 15
Unit level: Level 6
Teaching period(s): Semester 1
Offered by School of Computer Science
Available as a free choice unit?: Y




  • To introduce the main algorithms used in modern machine learning.
  • To introduce the theoretical foundations of machine learning.
  • To provide practical experience of applying machine learning techniques.

If you have sat an undergraduate ML course (particularly my COMP24111) then you may feel you know all this material. In fact we will cover virtually the same topics - however, you almost certainly will not have covered this material in the same depth as we will cover it. We will study why and how these methods work, at a very deep level. This is not a course on how to use ML techniques. It is a course on the foundations, the deeper aspects. If you really think you know it all already, then try sitting the previous exam papers, under exam conditions of course (i.e. no textbooks).


The world is filling up with data. Machine Learning is concerned with building mathematical models from this data, capable of tasks that would normally require a human. Typical applications might be spam filtering, speech recognition, medical diagnosis, or weather prediction. The data structures we use are known as "models" come in various forms, e.g. trees, graphs, algebraic equations, and probability distributions. The emphasis is on constructing these models automatically from data---for example making a weather predictor from a datafile of historical weather patterns. This course unit will introduce you to the concepts behind various Machine Learning techniques, including how they work, and use existing software packages to illustrate how they are used on data.

Teaching and learning methods


1 day per week (5 weeks)

Learning outcomes

Learning outcomes are detailed on the COMP61011 course unit syllabus page on the School of Computer Science's website for current students.

Employability skills

  • Analytical skills
  • Innovation/creativity
  • Project management
  • Oral communication
  • Problem solving
  • Research
  • Written communication

Assessment methods

  • Written exam - 50%
  • Written assignment (inc essay) - 50%


Topics covered:

  • Classifiers and the Nearest Neighbour Rule
  • Linear Models, Support Vector Machines
  • Algorithm assessment - overfitting, generalisation, comparing two algorithms
  • Decision Trees, Feature Selection, Mutual Information
  • Probabilistic Classifiers and Bayes Theorem
  • Combining Models - ensemble methods, mixtures of experts, boosting
  • Feature Selection - basic methods, plus some tasters of research material


  • Write a 6 page research paper applying appropriate ML techniques on supplied datasets.

Recommended reading

COMP61011 reading list can be found on the School of Computer Science website for current students.

Feedback methods

Face to face marking of preliminary work, on a weekly basis, plus written feedback on a larger project.

Study hours

  • Assessment written exam - 2 hours
  • Lectures - 10 hours
  • Practical classes & workshops - 20 hours
  • Independent study hours - 118 hours

Teaching staff

Gavin Brown - Unit coordinator

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