School of Computer Science BCS accreditation 2021 - 2026

Software Engineering wIE MEng (Hons) - COMP36212 Mathematical Systems and Computation

2.1.1 Knowledge and understanding of facts, concepts, principles & theories

The course develops knowledge and understanding of mathematical systems and their relation to computation. Aspects of precision, computer arithmetic, numerical algorithms and applications are all explored to understand their impact on mathematical modelling and simulation.

Assesement : Examination, Individual coursework

2.1.2 Use of such knowledge in modelling and design

The use of this course knowledge in modelling and design is through the ability to apply the techniques taught in this course across different disciplines within and outside computer science. For example when designing numerical analysis software for engineering applications.

Assesement : Examination, Individual coursework

2.1.3 Problem solving strategies

The course explores engineering problem solving from the perspective of the computational modeller. Core concepts and analysis tools including finite precision computation, floating point arithmetic, mixed precision algorithms and numerical solution of differential equations, are used to solve a range of problems along with their impact on computer performance.

Assesement : Examination, Individual coursework

2.1.4 Analyse if/how a system meets current and future requirements

This course has been designed with the main objective of making available to students research questions in the field. Topics studied are intended to equip students with the analytical problem-solving skills required to handle and exploit future developments of computer-based systems.

Assesement : Examination, Individual coursework

2.1.5 Deploy theory in design, implementation and evaluation of systems

The course addresses the problem of deploying appropriate theory, practices and tools for the specification, design, implementation and evaluation of computer-based systems. For example, by developing numerically stable and accurate algorithms and applying them across a range of example problems.

Assesement : Individual coursework

2.2.1 Specify, design or construct computer-based systems

This course specifies, designs and constructs computer models capturing mathematical systems defining real-world problems. Model implementations are analysed from the perspective of accuracy, stability, and computational performance.

Assesement : Examination, Individual coursework

2.2.2 Evaluate systems in terms of quality and trade-offs

This course provides students with a wide range of criteria to evaluate different types of systems, such as numerical precision, error, model convergence, and computer hardware requirements (e.g. memory, operations, energy).

Assesement : Examination, Individual coursework

2.2.4 Deploy tools effectively

The goal of the course is to provide students with the skills to define mathematical systems via computational models, and assess their efficacy against target objectives. This requires a range of skills including: defining a problem and casting it in a computational model, implementing a model in software, and analysing the output to measure performance. This process is explored across a range of problems from mechanical engineering, physics, and neural networks.

Assesement : Examination, Individual coursework

2.3.2 Development of general transferable skills

This course develops general transferable skills through the advanced multi-disciplinarily nature of topics studied. The development of problem-solving skills is explained in Section 2.1.3. Communication skills are developed through high participation in class, with creative and critical thinking encouraged in all teaching sessions. Technical writing is also developed in written report assignments.

Assesement : Examination, Individual coursework

3.1.4 Knowledge and understanding of mathematical and/or statistical principles

The course develops knowledge and understanding of mathematical systems and their solution through computational modelling. This includes aspects of numerical precision and accuracy on digital hardware, the use of numerical algorithms to approximate solutions to computationally hard problems, and exploration of these concepts under a range of engineering and science applications.

Assesement : Examination, Individual coursework

3.2.2 Defining problems, managing design process and evaluating outcomes

In this course students define problems, manage design processes and evaluate outcomes in the written assignments exploring accuracy and precision and solution of differential equations. The encourage students to consider not just the ‘correct’ answer, but to define and justify a solution which fulfils all problem requirements.

Assesement : Individual coursework

4.1.1 Knowledge and understanding of scientific and engineering principles

The course develops knowledge and understanding of scientific and engineering principles for the solution of practical problems: e.g. by developing numerically stable and accurate algorithms, and through assessing the impact of computational modelling choices on mathematical models. A wide variety of examples are provided to ground theory in real-world problems.

Assesement : Examination, Individual coursework

4.1.2 Knowledge and understanding of mathematical principles

The course develops knowledge and understanding of a range of mathematical principles commonly employed in computational modelling. For example, the numerical solution of systems described by differential equations, optimisation algorithms, and the use of bio-inspired systems such as neural networks. Application of these methods on computers is explored to understand and evaluate the interaction and optimisation of techniques and underlying hardware.

Assesement : Examination, Individual coursework

4.1.3 Knowledge and understanding of computational modelling

4.2.2 Defining problems, managing design process and evaluating outcomes

4.2.3 Principles of appropriate supporting engineering and scientific disciplines

In every part the course applies the principles of appropriate supporting engineering and scientific disciplines: machine learning and statistics, mathematics and theoretical computer science, and control theory, applied mathematics.

Assesement : Examination, Individual coursework