Course co-ordinator(s): Prof Katya Komendantskaya (Edinburgh).
Aims:
- 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.
Detailed Information
Course Description: Link to Official Course Descriptor.
Pre-requisites: none.
Location: Edinburgh.
Semester: 1.
Assessment Methods: Due to covid, assessment methods for Academic Year 2021-22 may vary from those noted on the official course descriptor. Please see the Computer Science Course Weightings and the Maths Course Weightings for 2020-21 Semester 1 assessment methods.
SCQF Level: 11.
Credits: 30.