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COMP24111: Machine Learning and Optimisation (2011-2012)

This is an archived syllabus from 2011-2012

Machine Learning and Optimisation
Level: 2
Credit rating: 10
Pre-requisites: COMP11120 or equivalent e.g. (MATH10662 and MATH10672 or MATH10111 and MATH10131 and MATH10212)
Co-requisites: COMP14112
Duration: 11 weeks in first semester
Lectures: 22 in total, 2 per week
Labs: 10 hours in total, 5 2-hour sessions, partly credited to COMP20910/COMP20920
Lecturers: Gavin Brown, Ke Chen
Course lecturers: Gavin Brown

Ke Chen

Additional staff: view all staff
Sem 1 Lecture 1.4 Tue 13:00 - 15:00 -
Sem 1 A Lab G23 Wed 09:00 - 11:00 H
Sem 1 A Lab UNIX Fri 09:00 - 11:00 G
Assessment Breakdown
Exam: 60%
Coursework: 0%
Lab: 40%

Themes to which this unit belongs
  • Learning and Search in Artificial Intelligence


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 methods for extracting rules or learning from data, and provide the necessary mathematical background to enable students to understand how the methods work and how to get the best performance from them. This course covers basics of both supervised and unsupervised learning paradigms and is pitched towards any student with a mathematical or scientific background who is interested in adaptive techniques for learning from data as well as data analysis and modelling.

Programme outcomeUnit learning outcomesAssessment
A1 A3 B3 D6Evaluate whether a learning system is appropriate for a particular problem.
  • Examination
A1 A3 B3 D6Understand how to use data for learning, model selection, and testing.
  • Examination
  • Lab assessment
A1 A3 B3 D6Understand generally the relationship between model complexity and model performance, and be able to use this to design a strategy to improve an existing system.
  • Examination
  • Lab assessment
A1 A3 B3 C4 D6Understand the advantages and disadvantages of the learning systems studied in the course, and decide which is appropriate for a particular application.
  • Examination
A1 A3 B3 C4 C5 D6Make a naive Bayes classifier and interprete the results as probabilities.
  • Lab assessment
  • Examination
A1 A3 B3 C4 C5 D6Be able to apply clustering algorithms to simple data sets for clustering analysis.
  • Lab assessment
  • Examination


Introduction to Machine Learning
K Nearest Neighbour Classifier
Decision Trees
Model Selection and Empirical Methodologies
Linear Classifiers: Perceptron and SVM
Na?ve Bayes Classifier
Basics of Clustering Analysis
K-mean Clustering Algorithms
Hierarchical Clustering Algorithms

Reading List

Core Text
Title: Introduction to machine learning (3rd edition)
Author: Alpaydin, Ethem
ISBN: 9780262028189
Publisher: MIT Press
Edition: 3rd
Year: 2014

Supplementary Text
Title: Pattern recognition (4th edition)
Author: Theodoridis, Sergios and Konstantonis Koutroumbas
ISBN: 9781597492720
Publisher: Elsevier
Edition: 4th
Year: 2009

Supplementary Text
Title: Artificial Intelligence: a modern approach (2nd edition)
Author: Russell, S. and P. Norvig
ISBN: 0130803022
Publisher: Prentice Hall
Edition: 2nd
Year: 2003