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Course content

This is a twelve-month MPhil programme, taught from within our Information Engineering Division, with a unique, joint emphasis on both machine learning and on speech and language technology. The course aims: to teach the state of the art in machine learning, speech and language processing; to give students the skills and expertise necessary to take leading roles in industry; to equip students with the research skills necessary for doctoral study.

See the website http://www.graduate.study.cam.ac.uk/courses/directory/egegmpmsl

Course detail

By the end of the programme, students will have acquired:

- a knowledge of the fundamental techniques in machine learning and how to apply these techniques to a range of practical problems;
- a deep understanding of the fundamental problems in speech and language processing and the technologies that form the current state-of-the art;
- a comprehensive understanding of techniques, and a thorough knowledge of the literature, applicable to the area of their chosen research topic;
- presentation skills through presenting their research in progress;
- the methodological and other technical skills necessary for research in their chosen area;
- the ability to critically assess the technical literature in machine learning and speech and language processing and related topics;
- directly marketable skills in computing, speech and language processing, machine learning, and the data sciences;
- collaborative skills through working with other students on the practical exercises and with PhD students and Research Assistants while carrying out their research project;
- experience in large-scale computing for machine learning and speech and language processing;
- an understanding of how to define and conduct a research project.


Students will spend the Michaelmas and Lent terms undertaking taught course modules. There will be an equivalent of ten 'full' core modules (ie, equivalent to a 16 lecture course), in addition to an elective option which can be chosen from a broad variety of modules. From mid-Lent term through the end of the course, students will conduct a substantial research project leading to a dissertation.

The taught modules will be in a range of styles: traditional lecture courses, lecture courses with associated practical classes, reading clubs, and seminar style modules. The course will emphasize coursework in several of the taught modules. Software projects aimed at implementing algorithms and modelling methods will be central to the practical modules and the research project.

Students can expect to receive reports at least termly on the Cambridge Graduate Supervision Reporting System. They will receive comments on items of coursework, and will have access to a University supervisor for their dissertation. All students will also have personal access to the Course Director and the other staff delivering the course.


Students will write a dissertation of no more than 15,000 words. An oral presentation will be compulsory, and will contribute to the assessment of the dissertation.

Several of the core courses are examined wholly or mainly through coursework. Some elective options will also be examined through coursework.

Several of the core courses are examined wholly or mainly through written examination. Some of the elective options will also be examined through written examination.

At the discretion of the Examiners, candidates may be required to take an additional oral examination on the work submitted during the course, and on the general field of knowledge within which it falls.


Students wishing to apply for continuation to the PhD would normally be expected to attain an overall mark of 70%.

How to apply: http://www.graduate.study.cam.ac.uk/applying

Funding Opportunities

There are no specific funding opportunities advertised for this course. For information on more general funding opportunities, please follow the link below.

General Funding Opportunities http://www.graduate.study.cam.ac.uk/finance/funding

Visit the MPhil in Machine Learning, Speech and Language Technology page on the University of Cambridge website for more details!





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Recipient: University of Cambridge

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