Postgraduate Course: Machine Learning Systems Project (80 credits) (INFR11272)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Dissertation |
Availability | Not available to visiting students |
| SCQF Credits | 80 |
ECTS Credits | 40 |
| Summary | This course provides a project write up for an individual project in Machine Learning Systems of 80 credits for students on the EPSRC CDT in Machine Learning Systems. |
| Course description |
This project is an individually write up of a personal (but likely collaborative) research project, and a proposal of work going forward. The focus of the assessment of this project is not particularly the
research itself, but the rigour, methodology, clarity and scholarly way in which that project was tackled. and the identification of future directions. Students will undertake a research project, and
provide a written report on that, and on future directions according to provided guidance. This will form part of the assessment of progression for CDT PhD students.
This course differs from the 60-credit course in having an assessment of how the potential benefits of collaboration, and how it benefits others across the ML-systems stack.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | CDT ML Systems students only. |
Course Delivery Information
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| Academic year 2024/25, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Full Year |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
800
(
Dissertation/Project Supervision Hours 30,
Programme Level Learning and Teaching Hours 16,
Directed Learning and Independent Learning Hours
754 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Coursework _100_% |
| Feedback |
The feedback provided to the students will be varied forms: (1) coursework feedback, (2) lecture content feedback, and (3) general course feedback. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- The capability of providing clear background knowledge of the literature surrounding the broader and specific area off study, that identifies the current research boundaries, and what problems are of interest.
- The ability to design a research programme in a justified way, engage in systematic, organised scholarly work according to a plan, and be able to provide meaningful conclusions to that work, and put in the work required to make progress in that programme (whether the outcome is successful or not).
- The ability to write research papers and monographs in a scholarly, clear, precise and unambiguous way that communicates well with both specialist and less-specialist readers.
- A concrete plan for future research that would provide a significant improvement to the current state-of affairs and be considered an excellent contribution. The student should demonstrate the ability to distinguish between valuable and less valuable ideas, and time-cost research with a reasonable level of adequacy. The student should demonstrate a point, and a clear direction and focus for future work.
- Both practical rigour (in experiments) and theoretical rigour (in explanation and/or modelling/analysis), and an understanding of potential benefits of collaboration, and how it benefits others across the ML-systems stack.
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Additional Information
| Graduate Attributes and Skills |
Research and Inquiry:
This project aims to significantly enhance students' capabilities in solving real-world problems, with
a strong emphasis on analytical and critical thinking skills. The students will undertake a complete
project from start to finish.
Personal Effectiveness:
Students must structure their time, plan their work, be systematic, assess and rethink and generally
managed a complete project. Projects are generally collaborative and good teamwork is required.
Supervisory training of students will be given in all these respects.
Personal Responsibility:
Students engage in independent learning. They will also be encouraged to go beyond standard
solutions, which will inspire creative thinking. They are responsible for their own work, and
convincing others of their approach. Students are responsible for listening to advices and heeding
good advice.
Communication:
Students are required to submit a written report and will receive ongoing feedback from
supervisors. This process will enhance written and oral communication and presentation skills.
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| Keywords | Machine Learning,Computer Systems |
Contacts
| Course organiser | Dr Amos Storkey
Tel: (0131 6)51 1208
Email: A.Storkey@ed.ac.uk |
Course secretary | Ms Lindsay Seal
Tel: (0131 6)50 5194
Email: lindsay.seal@ed.ac.uk |
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