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Department of Computer Science and Technology

MPhil ACS, Part III
Lent term
Course code: 
L90, L95 and L101 or similar for NLP topics. L330, LE49 or similar for Machine Learning topics


This course explores current research topics in machine learning and/or their application to natural language processing in sufficient depth that, at the end of the course, participants will be in a position to contribute to research on their chosen topics. Each topic will be introduced with a lecture which, building on the material covered in the prerequisite courses, will make the current research literature accessible. Each lecture will be followed by up to three seminar sessions which will typically be run as a reading group with student presentations on recent papers from the literature followed by a discussion.


Each student will attend 3 topics and each topic's sessions will be spread over 5 contact hours. Students will be expected to undertake readings for their selected topics. There will be some group work.

There will be a briefing session in Michaelmas term.


Students choose exactly three topics in preferential order from a list to be published in Michaelmas term. Students are assessed on one of these topics which may not necessarily be their first choice topic.

Topics offered in 2020-21:

  1. Imitation learning Dr A. Vlachos
  2. Do LSTMs learn Syntax  Prof. E. J. Briscoe
  3. SVM  Prof. M. Jamnik
  4. Graph neural networks Prof. P. Lio and Dr P. Veličković
  5. Machine Learning and Invariances Dr C. Misra
  6. Causal Inference Dr F. Huszár
  7. Bias in datasets Dr M. Tomalin
  8. Non-standard NLP Dr A. P. Caines
  9. Reinforcement learning Dr A. S. Prorok
  10. Gaussian processes Prof. N. D. Lawrence
  11. Applications of ML to Psychiatry Dr S. Morgan


On completion of this module, students should:

  • be in a strong position to contribute to the research topics covered;
  • understand the fundamental methods (algorithms, data analysis, specific tasks) underlying each topic;
  • and be familiar with recent research papers and advances in the field.


Students will work in groups to give a presentation on assigned papers. Each topic will typically consist of one preliminary lecture followed by 3 reading and discussion sessions. A typical topic can accommodate up to 9 students presenting papers. There will be at least 10 minutes general discussion per session.

Full coursework details will be published by October.


Coursework will be marked by the topic leaders and second marked by the module conveners.

  • Participation/attendance in three topics, 10%
  • Presentation (for one of the chosen topics), 20%
  • Topic coursework (for one of the chosen topics), 70%

Individual topic coursework will be published late Michaelmas term.

Please note that students will be assessed on one of their three chosen topics but this may not be their first choice.

Recommended reading

To be confirmed.

Further Information

Due to COVID-19, the method of teaching for this module will be adjusted to cater for physical distancing and students who are working remotely. We will confirm precisely how the module will be taught closer to the start of term.