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Department of Computer Science

Building Machine Learning Models Using Matrix Factorisation

Primary supervisor

Additional information

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Other projects with the same supervisor


  • Competition Funded Project (Students Worldwide)

This research project is one of a number of projects at this institution. It is in competition for funding with one or more of these projects. Usually the project which receives the best applicant will be awarded the funding. Applications for this project are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full department and project details for further information.

Project description

The decompositional approach is selected as one of "the top 10 algorithms with the greatest influence on the development and practice of science and engineering in the 20th century". Its core idea is to perform data analysis and learning by decomposing the input data matrix into simpler factors which are easier to observe, potentially better at a target machine learning task, and possess better interpretability. It serves as a fundamental building block in many machine learning algorithms widely used in practical fields. For instance, from the simple but effective approach principal component analysis, to more complex ones like collective factorisation.

The real-world data is usually noisy and contains missing information (partially observed), possesses complex structure, and is of large scale. These impose challenges on both model design and model optimisation. This project invites PhD candidates who are interested in applying linear algebra and numerical optimization techniques to advance machine learning by pursuing the decompositional path.

We will consider applicants who have:
*Very strong interest in the above research fields.
*An excellent undergraduate degree in Computer Science or Mathematics (or related discipline), and preferably, a relevant M.Sc. degree.
*Very good experience with computer programming of mathematical models and algorithms (in Python, Matlab, R or other platforms).
*Excellent report writing and presentation skills.
*Good ability to communicate with fellow students and colleagues.

Please, note that applicants must additionally satisfy the standard requirements for postgraduate studies at the University of Manchester, such as a first-class or high upper-second class (or an equivalent international qualification) and English language qualifications, as stated in the PGR guidelines.

Qualified applicants are strongly encouraged to informally contact the supervising academic Dr. Tingting Mu ( to discuss the application and possible research titles prior to applying.

Person specification

For information


Applicants will be required to evidence the following skills and qualifications.

  • This project requires mathematical engagement and ability substantially greater than for a typical Computer Science PhD. Give evidence for appropriate competence, as relevant to the project description.
  • You must be capable of performing at a very high level.
  • You must have a self-driven interest in uncovering and solving unknown problems and be able to work hard and creatively without constant supervision.


Applicants will be required to evidence the following skills and qualifications.

  • You will have good time management.
  • You will possess determination (which is often more important than qualifications) although you'll need a good amount of both.


Applicants will be required to address the following.

  • Comment on your transcript/predicted degree marks, outlining both strong and weak points.
  • Discuss your final year Undergraduate project work - and if appropriate your MSc project work.
  • How well does your previous study prepare you for undertaking Postgraduate Research?
  • Why do you believe you are suitable for doing Postgraduate Research?