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Department of Computer Science

Ensemble Strategies for Semi-Supervised, Unsupervised and Transfer Learning

Primary supervisor

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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

Traditionally there are two main paradigms in machine learning, supervised vs. unsupervised learning. A supervised learning algorithm uses teacher's information (labelled examples) to train a learner while unlabelled data are automatically categorised by an unsupervised learning algorithm without using teacher's information. In reality, however, labelled examples are often difficult, expensive, and/or time-consuming to obtain, which demands the efforts of experienced human annotators, while unlabelled data may be relatively easy to collect. Semi-supervised learning offers new techniques with the use of large amount of unlabelled data along with some labelled examples. In some situations, no labelled data are available so that one can only adopt the unsupervised learning paradigm for learning. Nevertheless, a common issue for both semi-supervised and unsupervised learning paradigms is how to exploit the information conveyed in unlabelled data. In a generic sense, the aforementioned learning problems may be naturally extended to transfer learning where other information sources can be explored to facilitate the current learning task in hand.

Ensemble learning studies machine learning algorithms and architectures that build collections of learners towards achieving better performance than an individual learner. This project is going to investigate typical ensemble learning methodologies, e.g., sequential and hierarchical combination of learning models, within the semi-supervised/unsupervised/transfer learning paradigms. The representation learning models that tend to tackle challenging real world problems that violate the standard yet conservative statistical assumptions made in the current machine learning algorithms. The main issues to be studied include theoretical/empirical investigation on novel ensemble representation learning framework including miscellaneous combination strategies in terms of generalization/stability and computational complexity, exploration/exploitation of unlabelled data or various information sources across different component learners and automatic model selection in the context of semi-supervised/unsupervised/transfer learning. In general, this project is suitable for one who is interested in fundamental research in machine learning while it is acceptable for one who already has a relevant application problem in mind and wishes to tackle their problems with an emerging technology such as ensemble learning. It is worth mentioning that this project description is generic and a specific project needs to be well-defined with a self-motivated student???s input.

In order to take this project, it is essential to have excellent mathematics and machine learning background knowledge as well as good programming skills. If you are interested in this project, please first visit my research student page: for the required materials and information prior to contacting me.

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?