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This is an archived syllabus from 2017-2018

COMP24111 Machine Learning and Optimisation syllabus 2017-2018

COMP24111 Machine Learning and Optimisation

Level 2
Credits: 10
Enrolled students: 197

Course leader: Ke Chen

Additional staff: view all staff


  • Pre-Requisite (Compulsory): COMP14112
  • Pre-Requisite (Compulsory): COMP11120
  • Pre-Requisite (Compulsory): MATH10111

Additional requirements

  • Students who are not from the School of Computer Science must have permission from both Computer Science and their home School to enrol.


    To enrol students are required to have taken  COMP11120  and COMP14112.  Or, if you are on a Computer Science and Maths programme you must have taken MATH10111.

Assessment methods

  • 60% Written exam
  • 40% Practical skills assessment
Sem 1 Lecture Simon TH A Mon 10:00 - 12:00 -
Sem 1 A Lab Toot (0 + 1) Thu 09:00 - 11:00 G
Sem 1 A Lab Toot (0 + 1) Fri 09:00 - 11:00 H
Sem 1 A Lab Toot (0 + 1) Mon 13:00 - 15:00 F
Themes to which this unit belongs
  • Learning and Search in Artificial Intelligence


Machine learning is concerned with creating learning models that allow a computer to exhibit behaviour that would normally require a human. Typical applications might be computer vision, speech recognition, machine translation, natural language processing, medical diagnosis, intelligent robots, and so on. The learning models come in various forms, e.g. parametric and non-parametric and probability distributions. The emphasis is on constructing these models automatically from data---for example making a face recogniser from a data file of facial images. 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 learning from data, and provide the necessary mathematical background to enable students to understand how the methods work, how to evaluate the performance a machine learning system 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.


  • Machine Learning Basics
  •  K Nearest Neighbour Classifier
  • Linear Classification/Regression
  •  Logistic Regression
  • Support Vector Machine
  • Deep Learning Models
  • Generative Models and Naïve Bayes
  • Basics of Clustering Analysis
  • K-mean Clustering
  • Hierarchical and Ensemble Clustering
  • Cluster Validation

Teaching methods


20 in total, 2 per week

2 hours of self revision


10 hours in total

Feedback methods

Face to face marking of all project work in lab

Study hours

  • Assessment written exam (2 hours)
  • Lectures (22 hours)
  • Practical classes & workshops (12 hours)

Employability skills

  • Analytical skills
  • Project management
  • Problem solving
  • Written communication

Learning outcomes

On successful completion of this unit, a student will be able to:

Learning outcomes are detailed on the COMP24111 course unit syllabus page on the School of Computer Science's website for current students.

Reading list

Introduction to machine learning Alpaydin, Ethem.9780262325745The MIT Press2014
Pattern recognition and machine learning Bishop, Christopher M., author.9780387310732Springer2006
Machine learning : a probabilistic perspective Murphy, Kevin P., 1970-9780262018029MIT Press©2012.
Machine learning Mitchell, Tom M. (Tom Michael), 1951-0070428077WCB/McGraw-Hill1997.

Additional notes

Course unit materials

Links to course unit teaching materials can be found on the School of Computer Science website for current students.