Fundamentals of AI (EIT CDT)

The course will provide you with training in both cutting-edge AI research methodologies and the development of business and transferable skills. 

You will undertake a significant, challenging and original research project, leading to the award of a DPhil.

Key topics and themes will focus on fundamentals of artificial intelligence, computational statistics, and machine learning, reflecting the breadth and depth of the research experience of the supervisory pool.

Course structure

Training will begin with an immersive three-week module that lays the foundation for a year-long, team-based open source software development project.

You will then undertake eight two-week core modules, taught during the first two terms, which will likely cover topics such as:

• deep learning
• causal inference
• generative AI
• bayesian inference
• data wrangling
• statistics and probability, and
• networks analysis.

Modules are designed to foster both in-depth learning and cohort-building, and rely on team work and collaboration. Best practices in sustainable and reproducible research are in all aspects of the scientific training programme.

After the taught modules, you will carry out two 10-week research projects under the supervision of academics from the supervisor pool. During this time, you will be based in the home department of your mini-project supervisors. Towards the end of the first year, you will select your DPhil research project which is likely to be a continuation of one of your short research projects.

Projects will focus on underpinning theory and method development of Artificial Intelligence and machine learning that will have the potential to have a transformative impact across a range of humane themes associated with the Ellison Institute of Technology (EIT):

• health and medical science;
• food security and sustainable agriculture;
• climate change and clean energy; and
• government innovation in the era of artificial intelligence.

The first two terms will be dedicated to intensive cohort training which will involve lectures, seminars and group work, all of which will take place on-site at the DTC. Further guest lectures and training opportunities will be organised for the full-cohort with opportunities for peer-to-peer learning.

Leadership and Innovation Programming Training

Students in the EIT CDT will also have access to leadership and innovation training to support them through to graduation and beyond. With a core focus on leadership and purpose, this training emphasises self-leadership, personal values and the skills needed to lead others and systems. Innovation and entrepreneurship are also central, providing students with access to top innovators and experts, along with opportunities to learn both the theory and practice of entrepreneurship. The training is delivered through a variety of formats, including expert talks, practical workshops and peer discussions.

Attendance

Resources to support your study

You will have access to computational facilities as well as a laptop. When you move out to your department you will also have access to the facilities provided by that department. You will remain a member of the CDT and be able to return to the CDT facilities, based within the Doctoral Training Centre, on Keble Road, to use the facilities there.

You will have access to seminars in all four departments as well as across the wider university. In addition to the training modules offered by the CDT, you will be able to sign up for a wide range of training courses and modules offered by departments across the university via the University's Researcher Training Tool.

You will also have access to Oxford's wide library network, including the recently refurbished Radcliffe Science Library.

Supervision

The allocation of graduate supervision for this course is the responsibility of the EIT CDT in Fundamentals in AI and it is not always possible to accommodate the preferences of incoming graduate students to work with a particular member of staff. Under exceptional circumstances a supervisor may be found outside the CDT.

During your first year, you will have a supervisor from the academic leadership of the CDT. Regular meetings serve to monitor academic progress as well as to discuss any academic issues or questions arising. After the first year, you will transition into the home department of your primary supervisor to commence you research project, though you may also have a co-supervisor in the same (or another) department. First-year supervisors will act as mentors throughout the programme, providing academic and pastoral guidance.

You will be expected to meet your supervisor on a regular basis. These meetings should take place at least once every two weeks, averaged across the year, to discuss your progress.

Assessment

Each mini-project will be assessed by researchers from the supervisor pool on the basis of a report written by the student. Modules are assessed by a presentation in small groups on some material studied during the two-week module (known as micro-projects within the programme).

All students will be initially admitted to the status of Probationer Research Student (PRS). Within a maximum of six terms, students will be expected to apply for transfer of status from Probationer Research Student to DPhil status.

A successful transfer of status from PRS to DPhil status will require the submission of a thesis outline. Students who are successful at transfer will also be expected to apply for and gain confirmation of DPhil status to show that their work continues to be on track. This will need to be completed within ten terms of admission.

Both milestones normally involve an interview with two assessors and therefore provide important experience for the final oral examination. Students will be expected to submit a thesis at four years from the date of admission.

The final thesis is normally submitted for examination during the fourth year and is followed by the viva examination. The final award for Oxford based students will be a DPhil awarded by the University of Oxford.

To be successfully awarded a DPhil you will need to defend your thesis orally (viva voce) in front of two appointed examiners.

Graduate destinations

This is a new course and there are no alumni yet. The CDT is dedicated to providing the organisation, environment and personnel required to develop a new generation of data scientists equipped to for a wide range of career paths in academia, research and industry.

College preference

The following colleges accept students on the EIT CDT in Fundamentals of AI: