This course introduces foundational concepts of single-variable calculus with a strong emphasis on their applications in data and artificial intelligence engineering. Topics include functions and mathematical modeling, limits and continuity, differentiation and its interpretation as sensitivity and rate of change, and basic integration as accumulation. Students learn to analyze and optimize single-variable functions commonly used as loss functions, cost models, and performance curves in data-driven systems. The course highlights the role of smoothness, convexity, and approximation in understanding model behavior, convergence, and robustness. Mathematical concepts are consistently connected to practical engineering contexts such as learning curves, system stability, and optimization of model performance.

This module covers core mechanics, oscillations, waves, fluid mechanics, and thermodynamics with a focus on analytical modeling relevant to data and AI engineering. Topics include Newton's laws, energy and momentum conservation, collisions, and center of mass analysis, alongside rotational dynamics, moment of inertia, and mechanical equilibrium. Periodic motion and simple harmonic motion are extended to wave and sound phenomena, illustrating signal-like behaviors common in physical systems. Students also explore fluid pressure, Bernoulli's equation, ideal gases, and thermodynamic processes. Throughout, the emphasis is on quantitative problem solving and mathematical modeling of physical systems.

Fundamental concepts of computer programming using Java. Data types, variables, and expressions. Input and output operations. Control structures including selection and iteration. Methods and parameter passing. Recursion and problem decomposition. Arrays and basic data structures. Searching and sorting algorithms. Program design, debugging, and testing. Development of simple applications through lectures and laboratory practice.

This module prepares students for future engineering modules and develops employability skills. Technical content includes CAD training centred around an engineering design project. Delivery consists of active learning sessions (seminars), small group work and supervised workshop sessions. Assessment consists of a design project report fulfilling a specific design brief based on sustainability of infrastructure (e.g. net-zero built environment).

This course extends calculus to multivariable settings relevant to modern data and AI systems. It covers vectors and geometry in high-dimensional spaces, multivariable functions, partial derivatives, gradients, and optimization techniques. Students study loss surfaces, feature sensitivity, constrained optimization, and regularization from a continuous mathematics perspective. Integration and coordinate transformations are introduced to support aggregation and representation of multidimensional data. Throughout the course, mathematical theory is linked to core data and AI engineering concepts such as gradient-based learning, optimization dynamics, feature interaction, and stability of complex systems.

This course covers electricity, magnetism, electromagnetic waves, and modern physics. Topics include electrostatics and magnetostatics, encompassing electric charge, fields, potential, and magnetic forces on charges and current-carrying conductors, alongside circuit analysis covering DC and AC circuits, capacitance, electromagnetic induction, and resonance. Maxwell's equations are introduced as the foundation for electromagnetic waves, with wave phenomena including reflection, refraction, interference, and diffraction. The course concludes with selected modern physics topics that shaped the development of quantum theory.

This module will provide an integrated suite of activities for students in each of the program areas within SEMS - aerospace, chemical, materials, biomedical, robotics and sustainable energy engineering. The module will introduce academic content and concepts relevant to the program stream selected by each student and will then cover four activities, where the application of the concepts, through the use of case studies/ideas, will be developed. The selection of activities will allow students to study those areas they are most interested in.

This module will centre around a problem-based learning framework which focuses on the development and implementation of experiments, with data analysis and reporting guided by examples, supplemented with supporting delivery to build on knowledge and content from the other modules in the first semester of the School of Engineering and Materials first year.

This course introduces fundamental statistical concepts and methods essential for data and artificial intelligence engineering, with an emphasis on practical interpretation and engineering decision-making rather than theoretical derivations. Topics include data summarization and visualization, probability and random variables, common probability distributions, statistical inference, and basic hypothesis testing. Students learn to reason about uncertainty, variability, and reliability in data, and to interpret statistical results in the context of data preprocessing, model evaluation, and system validation. The course emphasizes the use of statistics as a tool for understanding data behavior, assessing model performance, and supporting evidence-based decisions in robotics and AI engineering applications.

Basic unit of measurement, charge, current, voltage, power, electrical sources, Ohm’s law, Kirchhoff’s law, resistors in series and parallel circuits, voltage and current division calculation, circuit analysis using nodal, mesh (loop) and superposition, Thevenin and Norton theorems, maximum power transfer, capacitor and inductor, basic RL and RC circuits, basic RLC circuits, natural response of RL, RC, and RLC circuits, phasor analysis, AC circuit power analysis.

Major topics include the concepts of class, object, method, subclass, inheritance and their use in programming. The relevance of the object oriented style with respect to concrete software problems will be stressed both in lectures and labs. There will be two hours of lectures per week, and each student will have a weekly timetabled lab session. In addition, you will be expected to spend further time outside scheduled lab periods in the lab (or at home machines if they are available), and to read textbooks and review notes.

This module provide students with knowledge of mathematical and computational techniques that are essential for Engineering and Materials Science students. Mathematical topics covered are matrix algebra, eigenvalues and eigenvectors, differentiation, Taylor and Mclaurin series, basic limits, integration and partial derivatives. Students will be trained in command prompt applications of the numerical and symbolic toolboxes of Python. The mathematical and computational techniques will be developed through the introduction of the fundamental principles of statics for linearly elastic materials and their application to structures. It focuses on the behaviour of structures, in particular beams and shafts, and provides underpinning knowledge for a range of analyses on applications relevant to engineering.

This module builds on Computational and Mathematical Modelling 1 to provide students with knowledge of more advanced mathematical and computational techniques that are essential for Engineering students. Mathematical topics covered are differential vector algebra, vector calculus and introduction to ordinary differential equations. Students will further develop programming techniques using Python. The module will provide an introduction to the modelling and analysis of one-degree-of-freedom mechanical systems. It includes an analysis of the motion (kinematics) of particles and considers the forces causing these motions (kinetics) by the application of Newton's laws of motion. Analytical and computational methods for the solution of the differential equations describing the equations of motion will be studied.

This module covers how instrument and measurement systems work in an engineering context, and how each component in a measurement system functions enabling a measurement system to be designed and realised. This module reviews necessary fundamental knowledge in electrical circuits theory and signal processing principles, introduces the instrumentation visualised programming language such as LabVIEW and signal processing and data analysis software MATLAB. It will provide an opportunity for students to apply this knowledge in a problem-based learning exercise to design a robotic hand.

This module provides an introduction to the discipline of Artificial Intelligence, providing an overview of the different subfields of Artificial Intelligence, main methodologies and the problems they solve. This module also gives an introduction to the different ethical and philosophical aspects of Artificial Intelligence, such autonomous decision making systems, and a description of the role of Artificial Intelligence in society, its applications and the position this discipline takes in different organizations and businesses. The syllabus includes: What is Artificial Intelligence? Overview of the main AI techniques, including Expert Systems, Neural Networks and Deep Learning, Evolutionary Computation, Data Mining. Common AI applications include Big Data and applications, AI for Insurance, Financial Services, Security and Blockchain, and AI for Health. AI and Ethics covers Machine Ethics, Artificial Moral Agents, and use of AI within Social Media. AI and Law addresses legal frameworks around Artificial Intelligence. The module also includes industry talks from professionals and researchers applying AI in different disciplines.

This module will centre around a problem-based learning framework which focuses on the development and implementation of experiments, with data analysis and reporting guided by examples, supplemented with supporting delivery to build on knowledge and content from the other modules in the first semester of the School of Engineering and Materials first year.

The module introduces Robotics Engineering at basic to intermediate level and its application in various domains and fields. It will explore the evolution of robotics from conventional engineering perspective and also investigate the role of intelligent methods for the control and navigation of robotic systems. Current advancements in robotics will be presented. The module will include both theoretical and practical aspects with emphasis on experimental exploration of the mechanical and cognitive concepts related to Robotics. An overview of ethical and regulatory issues will be given within the module to highlight the importance of societal impacts now and in the future.

Computer-based control and monitoring systems and Edge AI, Hardware and software used, Computer software design and development for automation systems, Industrial standard protocols, Internet-of-Things, Web-based software development for control and monitoring systems, Computer and embedded systems data communication, Data management, Data analysis and visualization, Software development for Internet-based monitoring and control applications.

This course introduces students to the concept of MLOps (Machine Learning Operations), which combines machine learning, software engineering, and DevOps practices to streamline the development, deployment, and monitoring of machine learning models in production. We will cover key areas such as the ML project lifecycle, model deployment, experiment tracking, data pipelines, CI/CD, and more. By the end of the course, students will have practical experience in managing end-to-end ML projects and deploying models using industry-standard tools.

Basic signals and systems, a relationship between input signal and output signals, signal types and system types, Convolution, Continuous-time Fourier series and Fourier transform, the Laplace transform and its application to analyse continuous-time linear time invariant systems, Frequency response functions of linear time invariant systems, Sampling theorem, Discrete-time Fourier transform, Discrete Fourier transform, z-transform and z-transfer function representations, Frequency response functions of discrete-time linear time invariant systems, Introduction to filter design.

This module is focused on the basic principles of control systems analysis and design and its application to engineering systems with emphasis on mechanical, robotics and aerospace systems. It is within the scope of the linear time invariant system and frequency domain analysis and design method based on transfer functions. The students will design a control system for a particular application and gain experiences in analysis and design of a typical control system using numerical tools, such as MATLAB/SIMULINK, using the theoretical knowledge gained in lectures.

Understanding how each of the individual components of the nervous and muscular system function, and how they combine to generate successful movement enables us to develop a range of mobility technologies to improve lost locomotor function. Therefore, this module introduces the underlying fundamental principles of muscle force generation, neural signal transmission and the resultant gross motor movements. Within each aspect, consideration is given to both healthy and diseased states to demonstrate how different components affect functionality and the current technologies being used to improve functional loss.

The module covers the theoretical underpinnings and practical applications of Neural Networks and automatic differentiation as a tool for modern AI. Neural Networks & Deep Learning are now the method of choice for solving various Machine Learning problems. They are applied to several real-world problems not only within Academia but most importantly within Industry. Knowledge of Neural Networks and how to apply them to solve practical problems is now considered one of the most essential skills in the job market for a CS graduate. The module will include a detailed exposition for Neural Networks and their implementation using a Deep Learning framework. Topics include Automatic Differentiation, Stochastic Gradient Descent, Regression, Softmax Regression, Multi-Layer Perceptrons, Training of Neural Networks and hyper-parameter optimization, Convolutional Neural Networks, Recurrent Neural Networks, and applications to Vision and NLP.

This module will introduce mathematical and numerical methods that form the foundations to many fields in modern day scientific computing, covering mathematical modelling and simulation, big data science and artificial intelligence. It will cover the more involved concepts of differentiation and integration, extending to scalar and vector fields in multi-dimensional settings, together with some fundamentally important theorems. You will be introduced to the mainstream numerical approximations for integration and differentiation used in forming solutions to ordinary differential equations, optimisation, and root finding problems, and their implementation in python. The module will also include the fundamental concepts of big data processing and AI and will introduce and train the students in using current AI python tools.

Industrial training or short-term research conducted in industrial organizations, research institutes, or relevant professional environments. Students apply knowledge and skills in Robotics and Artificial Intelligence Engineering to real-world engineering tasks and problem-solving activities. The course emphasizes the use of modern engineering tools, digital platforms, and emerging technologies in workplace operations. Students gain practical experience in project execution, teamwork, communication, professional ethics, responsibility, and workplace safety while developing an understanding of organizational practices and professional standards. Assessment is based on performance evaluation, supervisor feedback, technical reports, and presentations.

The second year Design & Build Project is a group-based exercise planned for the whole module that mainly focuses on technical skills related to the design and development of a robotics-oriented project including hardware and software components and also the interfaces between these two sub-systems. The module will also introduce team work and group-based soft skills ranging from project planning and demonstration setting to project management and leadership. Generally, projects are divided into two phases with clear inter-link between the various components and tasks set for the groups.

Technology can support new forms of human communication. Embodied robotics, virtual avatars and social software applications (e.g. Twitter, Facebook and Flikr) create new forms of human interaction and new social economies ('crowdsourcing', 'prosumers', 'GPL licensing'). This research-led course introduces psychological theories of human communication that help us to understand how technology can enrich and transform human interaction. It also introduces the tools and techniques necessary for a principled approach to the design and evaluation of such technology.

Integrating the principles of advanced project development and research, this module engages students in a comprehensive, group-based capstone experience focused on designing and implementing artificial intelligence systems to solve complex, real-world problems for future technologies. Students apply data and AI engineering methodologies to develop high-fidelity solutions or conduct rigorous research investigations. The curriculum emphasises robust experimental planning and the evaluation of outcomes using technically sound protocols, while simultaneously fostering professional competencies such as collaborative teamwork, ethical awareness, and consideration of environmental and societal impacts. Throughout the module, students must demonstrate technical mastery and professional communication through structured oral presentations detailing the project’s progress, design, and final results.

Introduction to accounting and finance. Users of accounting information. Key characteristics of accounting information. Financial statement. Income statement. Preparing financial statement. Cash flow statement. Financial ratio indexes. Accounting Information Systems. Enterprise Resource Planning. Financial Accounting in manufacturing process. Fundamental of management accounting and cost accounting. Behaviors of costs. Full costing. Overheads. Job costing. Batch costing. Sources of finance. The role of stock exchange. Managing inventories. Receivables and cash. Investment decisions. Time horizon of plans and budgets. Business Report Systems. Business Intelligence and Business Analytics. Corporate Performance Management.

The module introduces both basic and advanced concepts related to the programming of robotic systems, for motion planning, visual perception and intelligent behaviors. It introduces state-of-the-art tools for robot programming, such as the Robot Operating System (ROS), discussing both theoretical and practical aspects. Computational techniques of kinematic analysis, machine learning, signal processing and control theory are introduced in the context of robotic systems. The importance of applying multidisciplinary techniques to enhance the current state of the art in Robotics is discussed, allowing students to explore creative and engineered solutions alongside conventional industrial applications.

This module introduces the building blocks of modern kinematics and modelling of robotic systems using the Euler-Lagrange and Newton-Euler methods. Holonomic and nonholonomic constraints are presented and used in the modelling of mobile robots. Software libraries and tools for building robot applications are introduced and implemented.

Integrating the principles of advanced project development and research, this module engages students in a comprehensive, group-based capstone experience focused on designing and implementing artificial intelligence systems to solve complex, real-world problems for future technologies. Students apply data and AI engineering methodologies to develop high-fidelity solutions or conduct rigorous research investigations. The curriculum emphasises robust experimental planning and the evaluation of outcomes using technically sound protocols, while simultaneously fostering professional competencies such as collaborative teamwork, ethical awareness, and consideration of environmental and societal impacts. Throughout the module, students must demonstrate technical mastery and professional communication through structured oral presentations detailing the project’s progress, design, and final results.

Introduction of Manufacturing Execution Systems (MES). Basic Manufacturing Line. Elements of Simple Process system: Entities, Attributes, Resources, and Queues. Definitions in Simple Process System: Work in Progress, Throughput, Cycle Time, Bottleneck rate, Raw process time. Randomness in manufacturing process. Random generator. Simulation of manufacturing process by ARENA software. Schedule rule and utilization. Plant Information System. Database system design by IDEF0 and IDEF1X. Dashboard for manufacturing performance. Computerized Maintenance Management System (CMMS).