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Machine Learning stream


The Machine Learning (ML) stream of the MPhil in Scientific Computing brings together the elements needed to make sense of scientific data. It is used in  many disciplines such as Physics, Chemistry, Astronomy, Financial Mathematics, Medicine, Bioinformatics and Robotics. The problems ML endeavours to solve are often put together under the term Big Data describing any large amount of structured, semi-structured and unstructured data. The techniques are widely used, for example in fraud detection, search engines, gaming and advertising.


Students following this stream should have a first degree in a numeric discipline such as mathematics, physics or engineering. An understanding of probability theory and statistics is a plus, but not essential, as is experience in a high level programming language.

Recommended reading

Christopher M. Bishop "Pattern Recognition and Machine Learning", Springer, 2007.

Kevin P. Murphy "Machine Learning: a Probabilistic Perspective", MIT Press, 2012.

David J.C. MacKay "Information Theory, Inference, and Learning Algorithms", Cambridge University Press, 2003.

David Barber "Bayesian Reasoning and Machine Learning", Cambridge University Press, 2012.

Lecture courses

Typically, students who are part of this stream are expected to attend the following lecture courses:

  • Machine Learning
  • Linear Systems
  • Fundamentals, Non-linear Systems, Interpolation
  • Introduction to Linux
  • Scientific Programming in C++
  • Software Design
  • GPUs for Scientific Computing
  • Message Passing Interface (MPI)
  • OpenMP
  • Computational Hardware
  • Programming for Power-efficient Computing


Typically, students who are part of this stream are expected to sit the following examination papers:

Paper 1: Fundamentals in Numerical Analysis (12hrs, 2units).

Paper 4: Linear Systems (12hrs, 2units).

Paper 7: Machine Learning (12 hours, 2 units).

Machine Learning Poster

Research Projects

The 2015-16 cohort investigated the following topics:

  • On the Analysis of HTS Data from SELEX Experiments

  • Compressive Sensing in Video Reconstruction

  • Detection Techniques for Drone Sense-and-Avoid Radar

  • The Model is Simple until Proven Otherwise – How to Cope in an Ever Changing World

  • Valuing Multidimensional Bermudan Put and Call Options

  • Mining Vehicle Electronic Control Unit (ECU) Data for Driver and Drive Cycle Identification

  • A Meta-Algorithm for Classification using Random Recursive Tree Ensembles: A High Energy Physics Application

  • Graph-Based Clustering: Distributed Algorithms for Balanced Graph Cuts

  • Sequential Matrix Completion

  • Deep Learning to Exploit Multi-Spectral Satellite Data for Landcover Mapping and Crop Classification