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Programme Specification (2021-2022)
Programme Information
Programme Title Programme Code HECoS Code
MRes AI and Machine Learning For Registry Use Only
For Registry Use Only
Award Length of Study Mode of Study Entry Point(s)
Total Credits
ECTS CATS
MRes 1 Calendar Year (12 months) Full-Time Annually in October
Ownership
Awarding Institution Imperial College London Faculty Faculty of Engineering
Teaching Institution Imperial College London Department Computing
Associateship N/A Main Location(s) of Study
South Kensington Campus
External Reference
Relevant QAA Benchmark Statement(s) and/or other external reference points Computing (Master’s)
FHEQ Level 7
EHEA Level 2nd Cycle
External Accreditor(s) (if applicable)
External Accreditor 1: N/A
Accreditation received: N/A Accreditation renewal: N/A
Collaborative Provision
Collaborative partner Collaboration type Agreement effective date
Agreement expiry date
N/A N/A N/A N/A
Specification Details
Programme Lead Professor Aldo Faisal
Student cohorts covered by specification 2021-22 entry
Date of introduction of programme October 21
Date of programme specification/revision June 21
Programme Overview
The MRes in AI and ML aims to train a new generation of Artificial Intelligence researchers and innovators and is designed to provide focussed AI training and a high-level, supervised research project that allows students to develop high-level analytical skills, and show their ability to design and lead projects.
AI is becoming increasingly pervasive across many sectors of business and public service, and this growth in application calls for people who combine theoretical grounding in AI with the ability to imagine, lead and deliver Research & Development (R&D) projects that meet exacting regulatory and real-world performance expectations, often working at the interface between AI and other disciplines. The 12-month MRes programme can be a more realistic initial commitment than a PhD for many graduates, people already employed in AI, and many employers - and for some it will also provide a pathway on to a Doctoral programme, able to start their research career with good publications during the MRes studies
The MRes in AI and ML will be a one-year full-time programme leading to the MRes award. The degree is built around one large research project to ensure students demonstrate the ability to manage research independently, learn the multidisciplinary approaches needed to bring AI/ML ideas into practice. Students on the programme would work on a wide range of AI topics, offering the opportunity to work at the leading edge in many areas of AI and across areas as well as numerous leading edge projects applying AI in domains such as health, business and finance, communications, and energy / product supply systems. You would also benefit from the cross-departmental setup, allowing interaction and exchange with students from other areas of AI and computer science in a supportive and inspiring environment; and with students in departments applying AI, to develop their interdisciplinary knowledge.
You would have made your choice of research project prior to beginning the programme. The programme involves taught-module lectures with appropriate assessment (coursework) and practical work in the first term, followed by full-time work on a research project with submission of an individual thesis at the end of the MRes year. The research project would normally be supervised by at least one AI expert, and often by more than one supervisor. Some students may also have a co-supervisor from industry.
You will also be required to complete the programme of professional skills development courses delivered by the Imperial College Graduate School, and would attend seminars and journal clubs throughout the year. A variety of seminars and workshops is provided to deepen and broaden the students’ research skill-base.
Learning Outcomes
Upon completion of the MRes in AI and Machine Learning you will be able to:
- Apply broad knowledge of state of the art AI and machine learning, to critically assess the strengths and weaknesses of a range of research and innovation approaches.
- Apply the principles of the law as well as understanding of responsible research and innovation , data protection, ethics and bias relevant to AI research and innovation
- Create software for advanced AI and machine learning using appropriate computing languages (e.g. Python) and frameworks (e.g. PyTorch, Tensorflow).
- Evaluate the research literature and other sources (e.g. patents, software) in depth their chosen field.
- In their field of in-depth study, identify key advances, uncertainties and opportunities in AI methods and the evidence on organisational, business and human factors for applications.
- Devise an AI research and development (R&D) proposal for a simulated business case from scratch,and present the proposal convincingly for decision-makers.
- Independently manage a substantial and novel R&D project and produce a thesis report to include analysis of leading-edge AI methods, evaluation of data sources and devising optimal approaches for AI development and for testing.
- Conduct an individual research project by managing time and responding to emerging findings and unforeseen challenges to ensure completion within time and resource limits.
● Seminars, symposia & workshops
E-learning & Blended Learning Methods ● Virtual Learning Environment: Blackboard ● Online discussions (Piazza) ● Online quizzes and interactive content ● YouTube videos
Project Learning Methods: ● Group and individual project work ● Conferences ● Symposia
Overall Workload Your overall workload consists of face-to-face sessions and independent learning. While your actual contact hours may vary according to the optional modules you choose to study, the following gives an indication of how much time you will need to allocate to different activities at each level of the programme. At Imperial, each ECTS credit taken equates to an expected total study time of 25 hours. Therefore, the expected total study time is 2250 hours per year for the 90 ECTS MRes programme.
For a typical 5 ECTS module, it is expected that around 28 hours would be spent in lectures, tutorials or labs, and ca 97 hours in independent study for the modules. For a typical 10 ECTS module, it is expected to spend ca 50 hours in lectures, seminars or labs and ca 200 hours in independent study. For the project, we expect that students spend 100 hours in research meetings and other research group related activities and 1, hours engaged in independent research studies.
Assessment Strategy
Assessment Methods
● Oral presentations including a presentation for non-technical audiences ● Coursework including multiple choice tests, practicals (exercises) and problem sheets ● Written reports, including a research thesis
The various formal assessments of the taught modules (coursework, practicals, and problem sheets) allow the students to apply acquired detailed knowledge and understanding of the essential concepts in the AI and machine learning field, including state-of-the-art coding algorithms, software frameworks, benchmarking datasets, and best practice.
Through the Research Tutorial’s assessments, students demonstrate the ability to conduct and assess critically scientific reviews and understand the wider AI domain/landscape. The module leader not only provides feedback on the academic performance, but also on the standard of academic writing and students’ presentation skills, which supports the development of professional and personal skills. (reminder: The
assessment includes leading a paper discussion, preparing the slides for the presentation, and writing a summary report). The research tutorials will also allow students to develop critical self-evaluation and how to respond to various opinions which are expressed in discussions.
After successfully completing the three assessment formats used for the Individual Research Project, students
shall be able to produce a complex research hypothesis, which was informed by their in-depth knowledge of AI algorithms, data set requirements and legal and ethical frameworks. More generally, students will be able to demonstrate self-direction and originality in tackling and solving problems; they learn how to act autonomously in planning and implementing tasks at a professional or equivalent level.
The module ‘Simulated R&D Proposal’ is assessed by written reports, allowing the student to check on their progress in identifying key advances, uncertainties and opportunities in AI methods and the evidence on
organisational, business and human factors for applications. The assessments require the students to develop and write a simulated business case from scratch, with expert decision-makers in mind as a target audience.
Year of MRes Coursework 20% Practical 15% Exam 5% Oral 20% Written 40%
This is followed by a meeting convened by the Programme Director/Deputy, to discuss: progress and any extra support needed; moderation of marks; and suitable scales of ambition and scope for the main projects. All measures are to assure that assessment outcomes are fair and reliable and that assessment criteria have been
applied consistently.
Academic Feedback Policy
Feedback may be provided in one of a number of formats, including:
- Oral (during or after lectures)
- Personal (discussion with academics and supervisors)
- Interactive (problem solving tutorials with GTAs & study groups)
Individual feedback is normally not provided on written examinations, if such are taken.
The College’s Policy on Academic Feedback and guidance on issuing provisional marks to students is available at: www.imperial.ac.uk/about/governance/academic-governance/academic-policy/exams-and-assessment/
Re-sit Policy
The College’s Policy on Re-sits is available at: www.imperial.ac.uk/student-records-and-data/for-current- students/undergraduate-and-taught-postgraduate/exams-assessments-and-regulations/
Mitigating Circumstances Policy
The College’s Policy on Mitigating Circumstances is available at: www.imperial.ac.uk/student-records-and- data/for-current-students/undergraduate-and-taught-postgraduate/exams-assessments-and-regulations/
Additional Programme Costs
This section should outline any additional costs relevant to this programme which are not included in students’ tuition fees.
Description Mandatory/Optional Approximate cost
N/A N/A N/A
Important notice : The Programme Specifications are the result of a large curriculum and pedagogy reform implemented by the Department and supported by the Learning and Teaching Strategy of Imperial College London. The modules, structure and assessments presented in this Programme Specification are correct at time of publication but might change as a result of student and staff feedback and the introduction of new or innovative approaches to teaching and learning. You will be consulted and notified in a timely manner of any changes to this document.
Progression and Classification
Award and Classification for Postgraduate Students
Award of a Postgraduate Degree (MRes) To qualify for the award of a postgraduate degree a student must have:
- accumulated credit to the value of no fewer than 90 credits at level 7 or above of which no more than 15 credits may be from credit level 6;
- and no more than 15 credits as a Compensated Pass;
- met any specific requirements for an award as outlined in the approved programme specification for that award.
Classification of Postgraduate Taught Awards
The College sets the class of Degree that may be awarded as follows:
- Distinction: The student has achieved an overall weighted average of 70.00% or above across the programme.
- Merit: The student has achieved an overall weighted average of above 60.00% but less than 70.00%.
- Pass: The student has achieved an overall weighted average of 50.00% but less than 60.00%.
a. For a Masters, students must normally achieve a distinction (70.00%) mark in the dissertation or designated final major project (as designated in the programme specification) in order to be awarded a distinction. b. For a Masters, students must normally achieve a minimum of a merit (60.00%) mark in the dissertation or designated final major project (as designated in the programme specification) in order to be awarded a merit c. Modules taken at level 6 as part of the programme specification for a named postgraduate award will contribute to the determination of pass, merit or distinction for any taught postgraduate award and are included in the calculation of the overall weighted average.
Programme Specific Regulations
N/A
Supporting Information
The Programme Handbook is available at: TBC
The Module Handbook is available at: TBC
The College’s entry requirements for postgraduate programmes can be found at: www.imperial.ac.uk/study/pg/apply/requirements
The College’s Quality & Enhancement Framework is available at: www.imperial.ac.uk/registry/proceduresandregulations/qualityassurance
The College’s Academic and Examination Regulations can be found at: www.imperial.ac.uk/about/governance/academic-governance/regulations
Imperial College is an independent corporation whose legal status derives from a Royal Charter granted under Letters Patent in 1907. In 2007 a Supplemental Charter and Statutes was granted by HM Queen Elizabeth II. This Supplemental Charter, which came into force on the date of the College's Centenary, 8th July 2007, established the College as a University with the name and style of "The Imperial College of Science, Technology and Medicine". www.imperial.ac.uk/admin-services/secretariat/college-governance/charters/
Imperial College London is regulated by the Office for Students (OfS) www.officeforstudents.org.uk/advice-and-guidance/the-register/
This document provides a definitive record of the main features of the programme and the learning outcomes that a typical student may reasonably be expected to achieve and demonstrate if s/he takes full advantage of the learning opportunities provided. This programme specification is primarily intended as a reference point for prospective and current students, academic and support staff involved in delivering the programme and enabling student development and achievement, for its assessment by internal and external examiners, and in subsequent monitoring and review.
Modifications
Description Approved Date Paper Reference
New Programme Programmes Committee 18/05/21 PC.2020.
