COMP0081 Applied Machine Learning

This database contains the 2018-19 versions of syllabuses.

Note: Whilst every effort is made to keep the syllabus and assessment records correct, the precise details must be checked with the lecturer(s).

Academic session



Applied Machine Learning



Module delivery

1819/A7P/T2/COMP0081 Postgraduate

Related deliveries

1819/A7U/T2/COMP0081 Masters (MEng)

Prior deliveries




FHEQ Level


FHEQ credits



Term 2

Module leader

Adamskiy, Dmitry


Adamskiy, Dmitry

Module administrator

Abbaro, Besheer


To give a detailed understanding of topics related to efficient implementation of large-scale machine learning with a focus on optimisation in both linear and non-linear machine learning models. Students will also gain experience in tackling real world problems through solving online machine learning challenges.A key aim is that students understand the challenges of optimisation and associated time and space complexities of various approaches.

Learning outcomes

On successful completion of the module, a student will:

  1. have a good understanding of practical issues arising in implementing machine learning in practice, including engineering challenges as well as the data ethics considerations;
  2. become familiar with techniques used in practice to solve real world machine learning problems and will be able to apply these techniques.

Availability and prerequisites

This module delivery is available for selection on the below-listed programmes. The relevant programme structure will specify whether the module is core, optional, or elective.

In order to be eligible to select this module as optional or elective, where available, students must meet all prerequisite conditions to the satisfaction of the module leader. Places for students taking the module as optional or elective are limited and will be allocated according to the department’s module selection policy.

Programmes on which available:

  • MRes Financial Computing
  • MRes Web Science and Big Data Analytics
  • MSc Business Analytics (with specialisation in Computer Science)
  • MSc Computational Statistics and Machine Learning
  • MSc Data Science (International)
  • MSc Data Science and Machine Learning
  • MSc Machine Learning
  • MSc Web Science and Big Data Analytics
  • MSc Scientific Computing
  • MSc Spatio-Temporal Analytics & Big Data Mining (and PGDip and Cert)
  • MSc Data Science


To be eligible to select this module, students must have:

  • understanding and abilities with Linear Algebra, Multivariate Calculus and Probability at mathematics FHEQ Level 4 (Undergraduate Year 1); and
  • familiarity with coding a high level language in order to complete assessments (strongly recommend that students are skilled in Python);

And, must have taken in Term 1:

And, are recommended to have taken in Term 1:

Please note that this module is not an introduction to machine learning.


  • Second Order Optimisation methods (Newton and Quasi Newton approaches and Conjugate Gradients)
  • Methods for solving Large Scale Linear, including Conjugate Gradients
  • Automatic Differentiation methods for efficiently computing first and second order gradients
  • Classical methods for Regression and Classification including linear and logistic regression
  • Clustering Methods for Unsupervised Learning
  • Matrix and Tensor Factorisation
  • Visualisation methods including tSNE
  • Ensembling, Gradient Boosting Machines
  • Data Ethics

An indicative reading list is available via


The module is delivered through a combination of lectures and self-directed learning.


This module delivery is assessed as below:



Weight (%)



Written examination (2hrs 30mins)




Challenege 1


LSA: alternative oral assessment


Challenge 2


LSA: alternative oral assessment


Challenge 3


LSA: alternative oral assessment

In order to pass this module delivery, students must achieve an overall weighted module mark of 50%.