MRes Individual Research Project - AI and Machine Learning

Module aims

The Individual Research Project is a major core component of the MRes programme and is designed to enable students to advance cutting-edge research on artificial intelligence (AI) and machine learning, by giving them the opportunity to develop and manage their own project over three terms. Students will need to develop and demonstrate a thorough understanding of AI research, including theory, methods and/or applications, in a focussed area of interest. They will be able to formulate and test hypotheses, conduct research responsibly and to high standards, and analyse the strength and validity of findings. Ultimately, students will develop their critical thinking and creativity, as well as their ability to adapt to emerging findings and problems.    

Learning outcomes

By carrying out their individual research projects, students will be able to:

  • Define a novel, cross-disciplinary research problem where AI is a core element, including but not limited to the areas of machine/deep learning, statistics-based methods, symbolic AI, and graph-based techniques.
  • Formulate and test hypotheses for developing and deploying new AI theories, algorithms, and applications.
  • Critically analyse academic literature relevant to the defined research topic and aligned fields.
  • Select and use appropriate methods, considering principles of research integrity, privacy, safety and security.
  • Communicate outcomes of the project effectively in written and oral academic formats and across different scientific disciplines.
  • Produce and present technical information in various formats: reports, presentations, progress meetings and thesis.

Module syllabus

This is an individual research project; topics explored will fit in the wider landscape of AI (e.g. machine/deep learning, statistics-based methods, symbolic AI) and can be driven by a plethora of application areas (e.g. medicine, transport, materials). Project work will allow the student to demonstrate what they have learned in AI ethics and law; programming for advanced AI; academic literature reviews; and the critical review of papers and issues in AI and applications in the multidisciplinary tutorials.    

Assessments

Poster Presentation    10%
Oral examination    20%
Individual Thesis    70%

Module leaders

Dr Yingzhen Li
Dr Amani El-Kholy