Sobre este curso

Oxford University
  • Data de início

    2020 - 2021
  • Fees

    £0
  • Study mode

    Full-Time
  • Ucas Code

  • Campus

    Oxford University
  • Qualification

    Doctor of Philosophy
Sumário

O melhor curso na melhor universidade para você

The information provided on this page was correct at the time of publication (November 2019). For complete and up-to-date information about this course, please visit the relevant University of Oxford course page via www.graduate.ox.ac.uk/ucas The Modern Statistics and Statistical Machine Learning CDT is a four-year DPhil research programme (or eight years if studying part-time). It will train the next generation of researchers in statistics and statistical machine learning, who will develop widely-applicable novel methodology and theory and create application-specific methods, leading to breakthroughs in real-world problems in government, medicine, industry and science. This is the Oxford component of StatML, an EPSRC Centre for Doctoral Training (CDT) in Modern Statistics and Statistical Machine Learning, co-hosted by Imperial College London and the University of Oxford. The CDT will provide students with training in both cutting-edge research methodologies and the development of business and transferable skills â?? essential elements required by employers in industry and business. Each student will undertake a significant, challenging and original research project, leading to the award of a DPhil. Given the breadth and depth of the research teams at Imperial College and at the University of Oxford, the proposed projects will range from theoretical to computational and applied aspects of statistics and machine learning, with a large number of projects involving strong methodological/theoretical developments together with a challenging real problem. A significant number of projects will be co-supervised with industry. The students will pursue two mini-projects during their first year (specific timings may vary for part-time students), with the expectation that one of them will lead to their main research project. At the admissions stage students will choose a mini-project. These mini-projects are proposed by our supervisory pool and industrial partners. Students will be based at the home institution of their main supervisor of the first mini-project. During their first three months (six months for part-time students) at the CDT students will work on their first mini-project, and during months four to six (seven to twelve months for part-time students) of their DPhil they will work on a second mini-project. For students whose studentship is funded or co-funded by an external partner, the second mini-project will be with the same external partner but will explore a different question. Each mini-project will be assessed on the basis of a report written by the student, by researchers from Imperial and Oxford. The students will then begin their main DPhil project, which can be based on one of the two mini-projects. The final thesis is normally submitted for examination during the fourth year (or eighth year if studying part-time) and is followed by the viva examination. Where appropriate for the research, student projects will be run jointly with the CDTâ??s leading industrial partners, and you will have the chance to undertake a placement in data-intensive statistics with some of the strongest statistics groups in the USA, Europe and Asia. Alongside their research projects students will engage with taught courses each lasting for two weeks. Core topics will be taught during their first year (specific timings may vary for part-time students) and are: Students will also be required to take a number of optional courses throughout their four years, which could be made up of choices from the following list: Advanced Monte Carlo methods, Causality and Graphical models, Networks, Nonparametric Bayes, Modern Asymptotics, Optimisation, (Deep) learning Theory and Practice, Reinforcement learning and Multi-Armed Bandits, Applied statistics and Genetics/computational biology.

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