Postgraduate Course: Credits Awarded for Dependable and Deployable Artificial Intelligence for Robotics 1 (INFR11308)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 30 |
ECTS Credits | 15 |
| Summary | This is a placeholder course designed to record marks for the courses delivered and ratified via Heriot Watt University as part of the Dependable and Deployable Artificial Intelligence for Robotics CDT. |
| Course description |
This course aims to:
- Introduce students to the principles of specification and implementation of autonomous systems that have machine learning/AI components.
- Ensure that CDT-D2AIR educates a cohort of specialists who understand both the complexities of autonomous system specification and
verification, and existing solutions and trends.
- Expose the students to advanced principles of specification-based design. Systems engineering is a vital discipline in the development of dependable and deployable systems, with a core element being the creation and adherence to a clear specification.
- Expose students to the state-of-the-art in modern verification technologies, including the tools for verification of machine learning and hybrid systems (e.g., SMT solvers, model checkers, functional programming languages, neural network verifiers).
- Ensure that students understand the complexities of verification of autonomous systems with machine learning components. In particular, that they know and understand algorithms and tools that provide the supporting infrastructure for verification, i.e., specification-driven training (as opposed to data-driven training), automated ways to connect verification of machine-learning components with verification of the overall system.
- Students will learn how to apply these methods to various aspects of robotics, throughout the design and development pipeline.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
|
| Academic year 2026/27, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
300
(
Programme Level Learning and Teaching Hours 6,
Directed Learning and Independent Learning Hours
294 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
This course is delivered and ratified by Heriot Watt University as part of a joint programme and this course instance allows for students to have their credits recorded here at the University of Edinburgh. |
| Feedback |
n/a |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Implement principles of specification-based design in robotics applications
- Formulate properties of autonomous systems with machine learning components
- Contrast the properties and vulnerabilities of autonomous systems with machine-learning components with traditional autonomous system design.
- Critically assess and deploy state-of-the-art tools for verification of neural networks.
- Deploy state-of-the-art machine learning methods that optimise models for the given specification.
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Additional Information
| Graduate Attributes and Skills |
n/a |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Mohan Sridharan
Tel:
Email: m.sridharan@ed.ac.uk |
Course secretary | Ms Lindsay Seal
Tel: (0131 6)50 5194
Email: lindsay.seal@ed.ac.uk |
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