Postgraduate Course: ML Systems Internship Research and Engagement Report (INFR11275)
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 | 30 |
ECTS Credits | 15 |
| Summary | Students will do an (approximately) 4 month internship with a company, or equivalent. Students will write up their experience of the internship in terms of the difference in emphasis between the needs and requirements of a general company environment, and the needs and requirements of a University research environment. They will consider their work on the PhD so far an elaborate on how it can be developed for broader impact, company interest and where starting with a demand-driven and market-driven perspective would lead. |
| Course description |
Students will do an (approximately) 4 month internship with a company, or alternative form of engagement with external partners, bodies or stakeholders. This course will involve a reflection and write up their experience of the internship in relation to the PhD study. The work will be a supervised self-study and reflection. Due to typical confidentiality arrangements, there is no expectation of a technical reflection on the content of the internship. Rather, it will be a reflection on the experience of the internship and the impact of that on research engagement.
The report will cover:
- Reflections on the difference in emphasis between the needs and requirements of a general company environment, and the needs and requirements of a University research environment
- Reflection on the downstream effects of these differences on the progress of work, the longer term impact, the dissemination and communication of the work etc.
- Reflection on the research work so far during the PhD, and company interest in that work can be enhanced, and where starting with a demand-driven and market-driven perspective would lead.
- Reflection on how to increase the impact of the PhD research so far, and the best approach to driving commercial and/or social use of the work in practical settings.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | CDT ML Systems students only. |
Course Delivery Information
| Not being delivered |
Learning Outcomes
On completion of this course, the student will be able to:
- An ability to delineate the needs and requirement of research and corporate/other environments.
- An ability to reflect on research in the context of its broader impact.
- Clarity about what demands there are from stakeholders in the area of the PhD.
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Additional Information
| Graduate Attributes and Skills |
Reflection:
This enhances students' capabilities in understanding the difference in needs across various sectors of society, reflecting on experience.
Direct Engagement with Stakeholders:
The internship will enable students to directly engage with relevant companies etc.
Personal Responsibility:
Students will arrange appropriate internships. They are responsible for their own work, and their work within the company.
Communication:
Students are required to submit a written report and communicate well. |
| Keywords | Machine Learning,Computer Systems |
Contacts
| Course organiser | Dr Amos Storkey
Tel: (0131 6)51 1208
Email: |
Course secretary | Ms Lindsay Seal
Tel: (0131 6)50 5194
Email: |
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