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Current postgraduate taught students

COMP61011: Machine Learning and Data Mining (2011-2012)

This is an archived syllabus from 2011-2012

Machine Learning and Data Mining
Level: 6
Credit rating: 15
Pre-requisites: No Pre-requisites
Co-requisites: No Co-requisites
Lectures: 1 day per week (5 weeks)
Lecturers: Gavin Brown
Course lecturer: Gavin Brown

Additional staff: view all staff
Sem 1 P1 Lecture 2.19 Thu 09:00 - 13:00 -
Sem 1 P1 Lab 2.25b Thu 13:00 - 17:00 -
Assessment Breakdown
Exam: 50%
Coursework: 50%
Lab: 0%

Themes to which this unit belongs
  • Learning from Data
  • Text Mining
  • Visual Computing


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.


This course unit aims to introduce the main algorithms used in modern machine learning, to introduce the theoretical foundations of machine learning and to provide practical experience of applying machine learning techniques.

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
- Decision Trees, Feature Selection, Mutual Information
- Probabilistic Classifiers and Bayes Theorem
- Combining Models - ensemble methods, mixtures of experts, boosting
- Algorithm assessment - overfitting, generalisation, comparing two algorithms

Write a research paper applying appropriate techniques on supplied datasets.