The subject covers the linear algebra required for post-graduate engineering courses. The learner will gain the expertise to interpret the linear algebra models used in the engineering literature. It will also enable learners to model problems using linear algebra methods.

1. Solve systems of linear equations and analyse the solutions.

2. Analyse affine transformations in three dimensions.

3. Interpret the linear algebra in state of the art research publications andreproduce findings.

4. Explain the use of vector spaces in analysing solutions to systems of equations.

5. Use projections to find the least squares solution of overdetermined systems.

6. Decompose matrices into their singular value decompositions and interpret.

7. Apply matrices to the Fourier transform, graphs and networks.

ADAS and Autonomous System Architecture provides the learner with an appreciation for the bigger picture of the automotive industry. The student will gain an understanding of the multi-disciplinary nature of the industry, as well as knowledge of its supply chain. Different system architectures and design constraints are introduced.

1. Demonstrate an understanding of the automotive landscape, including its supply chain, and the associated roles and responsibilities.
2. Assess Automotive System Architectures, past, present and future. Create an accurate conclusion on where the technology is currently at, both from hardware and software viewpoints.
3. Apply the theory of Vehicle Networking to critically analyse the data flow of increasingly complex future vehicle networks.

4. Conduct an analysis of the design constraints within automotive and use this information to appraise the decision-making behind current design.
5. Effectively collaborate and communicate with the engineering community on what is topical in the automotive industry.

This module investigates the physical and technical foundations, strengths and weaknesses of sensors for advanced driver assistance systems and their applications in environment detection in automotive vehicles.

1. Assess optical radiation, radiometric and photometric quantities.

2. Explain the physical and technical foundations of visible and infrared spectrum camera systems and their role in the automotive environment.

3. Summarise the functional characteristics and properties of modern Lidar, Radar, Ultrasonic and other relevant sensor systems and their applications in environment detection.

4. Evaluate the strengths and weaknessess of sensing technologies for specific applications in autonomous vehicles.
5. Select and develop suitable detection systems for specific applications in the automotive environment.

This module looks at the computer vision required to understand the structure of a real-world scene given several images of it.

Introduces key 2D-Image Processing, segmentation and features detection techniques, camera intrinsic and extrinsic parameters and multiple view geometries.

1. Select and apply 2D Image processing techniques to appropriate problems.
2. Evaluate the applicability of various 3D image processing techniques to specific problems, based on the review of key academic papers.
3. Create or reconstruct 3D Scene geometries using various methods.
4. Effectively collaborate and communicate with others in the timely development of solutions to Computer vision problems, including reports and software.
5. Identify the key metrics that are used to measure and compare the effectiveness of state of the art computer vision techniques, and use these metrics to evaluate the performance of emerging computer vision techniques previous state of the art.

Introduces students to 'Automotive System Safety and Cybersecurity' and concepts relevant for Advanced Driver Assistance Systems and Self Driving Cars.

1. Illustrate a detailed knowledge and understanding of the current driver assisted and cyber security relatedautomotive industry standards.

2. Evaluate the essential end-to-end components of a functional safety system containing electrical, electronic and programmable elements systems.

3. Articulate an understanding of the taxonomy and definitions for terms related to on-road motor vehicle automated driving systems
4. Review and investigate the impact of thepertinent Data Protection Regulation on the autonomous vehicle supply chain.

5. Design a system capable of incorporating the latest legislative structure and requirements pertaining to certification of Automotive System Safety and cybersecurity.

This module covers the statistics and probability required for a Masters in Engineering. The learner will gain the expertise to interpret the probabilistic models used in the engineering literature. It will cover statistical methods to analyse and quantify processes. It will enable learners to model problems using probabilistic and statistical mathematical methods.

1. Apply probability theory to analsye the centrality,dispersion and relationships withinand between datasets and distributions.

2. Apply experimental design and statistical inference to make inferences from data.

3. Analyse the bias and variance of maximum likelihood and Bayesian estimators.

4. Analyse stochastic processes (including Markovprocesses).

5. Evaluate, select and apply appropriate statistical techniques to problems in the application field of study.

6. Interpret the probability and statistics used in state of the art research publications and reproduce findings.

7. Model an application specific problem with statisticsand probability techniques.