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Current postgraduate taught students

COMP61011: Foundations of Machine Learning (2012-2013)

This is an archived syllabus from 2012-2013

Foundations of Machine Learning
Level: 6
Credit rating: 15
Pre-requisites: No Pre-requisites
Co-requisites: No Co-requisites
Lectures: 1 day per week (5 weeks)
Course Leader: Gavin Brown
Course leader: Gavin Brown

Additional staff: view all staff
Sem 1 w1-5 Lecture 2.19 Wed 09:00 - 13:00 -
Sem 1 w1-5 Lab 2.25b Wed 13:00 - 17:00 -
Assessment Breakdown
Exam: 50%
Coursework: 50%
Lab: 0%

Themes to which this unit belongs
  • Learning from Data
  • Information Management 1


Machine learning is concerned with creating mathematical "data structures" that allow a computer to exhibit behaviour that would normally require a human. Typical applications might be spam filtering, speech recognition, medical diagnosis, or weather prediction. The data structures we use (known as "models") come in various forms, e.g. trees, graphs, algebraic equations, 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 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. The course has a fairly mathematical content although it is intended to be self-contained.


- 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).

Programme outcomeUnit learning outcomesAssessment
G1Have knowledge and understanding of the principle algorithms used in modern machine learning, as outlined in the syllabus.
  • Lab assessment
  • Examination
G1Have sufficient knowledge of information theory and probability theory to understand some basic theoretical results in machine learning.
  • Examination
G3Be able to apply machine learning algorithm to real datasets, evaluate their performance and appreciate the practical issues involved.
  • Lab assessment
G4Be able to provide a clear and concise description and justification for the employed experimental procedures.
  • Lab assessment


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.

Reading List

All material is provided in a self-contained course text written by the module leader.
This will be provided on starting the module.