Students will attend a combination of interactive lectures and practical workshops designed to provide theoretical knowledge and understanding with experiential learning by applying key concepts, tools and models in innovative ways. Lectures will predominantly explore key business enterprise and development concepts, models, theories and examples whereas workshops will enable students to practically apply these business insights to their specific area of study through individual and group work. As part of the learning experience, formative and summative assessments give students the opportunity to discuss their work with tutors and gain valuable feedback as they develop and apply their learning in ‘real-life’ entrepreneurial contexts. 

This module offers an in-depth exploration of artificial intelligence (AI) and its transformative role in the development of advanced software systems. It introduces key theoretical approaches and practical techniques for designing and deploying intelligent technologies, empowering you with the skills to build AI-driven solutions.

Key topics covered include:

• Introduction to Artificial Intelligence: Understanding the foundations of AI and its significance in modern software development.
• Theoretical Approaches to AI: Exploring algorithms and models that underpin intelligent systems, such as decision trees, neural networks, and reinforcement learning.
• Practical AI Implementation: Gaining hands-on experience with AI techniques, including machine learning, natural language processing, and computer vision, through coding exercises and projects.
• Designing and Deploying Intelligent Systems: Examining methods for building robust, scalable, and ethically sound AI technologies.
• AI in Various Domains: Critically evaluating how AI is applied across industries such as business, healthcare, education, law, government, and scientific research, along with the ethical and societal implications of these applications.

This module blends theory with practical application, equipping you to develop intelligent systems and critically assess their impact in a wide range of real-world contexts.

Recognition of the need to apply data science applications in organisations.

Establishment of correct selection and application of data science techniques (i.e. data shaping, model type selection, testing and application) in various organisational contexts. 

Application of common machine learning tools (e.g. logistic regression, non-linear model estimation, neural network) in a common development environment (e.g. R, Python, Scala) in preparation for the real world context.

Evaluation of the role of ethics in the application of data science techniques.

Students will undertake a large self-directed software project in a specialist topic of their choice with guidance and support from a dedicated academic supervisor.

The project will begin with an appraisal of said topic, usually through a literature review and/or a commercial assessment of viability. This will be followed by planning and creation of a practical software artefact covering an implementation lifecycle, making use of project management techniques.

Ethical issues will be explored, leading to required approval for quantitative and/or qualitative testing, with results then analysed and used to inform futher development and to draw conclusions against a hypothesis.