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Department of Computer Science and Technology

Masters

 

Course pages 2022–23

Advanced topics in machine learning

Principal lecturers: Prof Mateja Jamnik, Dr Carl Henrik Ek
Taken by: MPhil ACS, Part III
Code: R255
Hours: 16
Prerequisites: L48, L101, L46 or similar

Aims

This course explores current research topics in machine learning 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.

Structure

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.

Syllabus

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.

The topics to be offered in 2022-23 are yet to be decided but to give an indicative idea of the types of topics, the ones offered in 2021-22 were:

  1. Imitation learning Dr A. Vlachos
  2. Applications of Machine Learning to Psychiatry Dr S. Morgan
  3. Federated Learning Dr N. Lane
  4. Reinforcement learning Dr A. Prorok
  5. Machine Learning of self-organizing structures: from textures to developmental biology Dr B. Dumitrascu
  6. Causal Inference Dr F. Huszár
  7. Bias in datasets Dr M. Tomalin
  8. Probabilistic Numerics: Computation as statistical inference Dr C. H. Ek

Objectives

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.

Coursework

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.

Assessment

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 infectious respiratory diseases, the method of teaching for this module may be adjusted to cater for physical distancing and students who are working remotely. Unless otherwise advised, this module will be taught in person.