Module ENGI47615: Optimisation and Control for Artificial Intelligence

Department: Engineering

ENGI47615: Optimisation and Control for Artificial Intelligence

Prerequisites

Corequisites

Excluded Combination of Modules

Aims

• This module is designed solely for students studying Department of Engineering degree programmes.
• To understand optimisation and control techniques that can be used to improve AI-driven engineering systems.
• To give students the tools and training to recognize and formulate optimisation and control problems that arise in AI applications.
• To present the basic theory of such problems, concentrating on results that are useful in AI-driven applications and computation.
• To give students a thorough understanding of how such problems are solved in AI contexts, and practical experience in solving them.
• To provide students with the background required to use optimisation and control methods in their own AI research work or applications.

Content

• Optimisation theory and techniques for AI applications.
• Model Predictive Control (MPC) theory and implementation.
• Applications of optimisation and MPC in AI-driven engineering systems.
• Integration of machine learning techniques with optimisation and control.

Learning Outcomes

• A knowledge and understanding of optimisation and control theory and techniques as applied to AI-driven systems.
• AHEP4 Learning Outcomes: In order to satisfy Professional Engineering Institution (PEI) accreditation requirements the following Accreditation of Higher Education Programmes (AHEP4) Learning Outcomes are assessed within this module:
• M1. Apply a comprehensive knowledge of mathematics, statistics, natural science and engineering principles to the solution of complex problems (coursework assessed).
• M2. Formulate and analyse complex problems to reach substantiated conclusions. This will involve evaluating available data using first principles of mathematics, statistics, natural science and engineering principles, and using engineering judgment to work with information that may be uncertain or incomplete, discussing the limitations of the techniques employed (coursework assessed).
• M3. Select and apply appropriate computational and analytical techniques to model complex problems, discussing the limitations of the techniques employed (coursework assessed).

• An awareness of current analysis methods in AI optimisation and control along with the ability to apply those methods in novel situations.
• An in-depth knowledge and understanding of specialised and advanced technical skills in AI-driven optimisation and control, an ability to perform critical assessment and review, and an ability to communicate the results of their own work effectively.

• Capacity for independent self-learning within the bounds of professional practice in AI and engineering.
• Highly specialised numerical and computational skills appropriate to an AI engineer.
• Mathematics relevant to the application of advanced AI concepts in engineering optimisation and control.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

• The Optimisation module is covered in lectures, and reinforced by problem sheets, leading to the required problem solving capability.
• Two hour lectures delivered in a single term, structured as one lecture of methods followed by one lecture of exercises. The methodology taught in the first hour would be immediately followed by a second hour of exercises to consolidate student knowledge and understanding of optimisation theory and techniques.
• Coursework (portfolio) is appropriate as a mode of assessment for this module because it allows students to work on realistic engineering problems.
• Students are encouraged to engage with staff Office Hours for one‑to‑one or small‑group discussion of any aspect of the module. These sessions are offered weekly during teaching, timings are published on Learn Ultra.

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment:

■ Students who do not attend monitored activities shown under Teaching Methods and Learning Hours, or who fail to complete the summative or formative assessment(s) specified above, may be subject to the Academic Progress procedures defined in the University's General Regulation V, and may be required to leave the University.