Applied Artificial Intelligence MSc

This module is intended to provide a general introduction to the analytical techniques needed for artificial intelligence and is tailored for you to develop core maths knowledge that artificial intelligence is built upon. The module will enable you to assess your existing mathematical skills and sympathetically enable you to gain knowledge and skills and/or remedy any basic deficiencies.

The learning topics mainly cover the following areas: basic functions, variables, equations and graphs; linear algebra; probability and statistics. These topics will enable you to have a solid grasp over the most essential maths concepts for machine learning and to explore applying the core mathematical models to problems of regression and classification in a real-life work environment.

This module introduces high-level computer programming using Python, one of the most powerful yet intuitive programming languages. It is designed to benefit students with no prior programming experience.

This module enables you to understand the underlying concepts, principal components, design elements of computer programming and then it gradually leans towards technical aspects of programming by putting theoretical programming concepts into practice.

This module employs real-world scenarios and case studies from various industrial domains and practices such as healthcare, marketing, finance, business, etc. to introduce programming concepts and skills. Best programming practice will be taught and used to ensure a maximum level of productivity and quality. In addition, methods and techniques will be introduced and applied to the validation and verification of software quality and standards.

Machine learning is a sub-discipline of the Artificial Intelligence that deals with teaching the computer to act without being programmed. In this module, you’ll learn about the tools and algorithms that can be used to create machine learning models. We’ll also investigate how large data sets should be divided into a training set and a test set. Different types of problems that can be solved with machine learning will be introduced; supervised learning algorithms and examples of where they can be applied will be presented. A range of parametric algorithms such as linear regression, logistic regression, and non-parametric algorithms such as K-Nearest neighbour, decision trees, SVMs, will be discussed.

To be able to evaluate a model, a few performance metrics will be explored. The metrics chosen have influence on how the performance of machine learning algorithms is measured and compared. An important concept that we must be aware of when training machine learning algorithms is 'overfitting'. You will learn to apply regularisation techniques to mitigate overfitting and enhance model performance.

You’ll investigate and experiment with the various models and algorithms covered in the module using standard libraries such as scikit-learn, statsmodels and Python ML packages, gaining practical skills that are directly applicable in real-world scenarios.

This module supports students in the preparation and submission of a Masters Stage Dissertation or Project worth 60 credits.

The topic will be based around a project relevant to your specialism and experience. It will be completed solely by yourself as an individual project and, if part of a larger project, the aspect implemented must be clearly identifiable as your sole work. Group projects are not allowed.

The project outline will be formulated and agreed after discussion between yourself and a supervisor at ARU and must meet the objectives of an ARU postgraduate major project.

During the implementation, you will be expected to identify / formulate the problem and issues, conduct literature reviews, evaluate information, investigate and adopt suitable development methodologies, determine solutions, develop hardware/software and/or media artefacts as appropriate, process data, critically appraise and present your findings using a variety of media.

Regular meetings with the project supervisor and workplace mentor should take place, so that the project is closely monitored.

The rise of Generative Artificial Intelligence (AI) is undoubtedly one of the biggest technological breakthroughs in modern times. It is provoking a major rethink in many fundamental aspects of our lives including the balance of work between humans and machines, and the foundational knowledge, skills, and abilities that modern humans ought to learn and develop in the era of Generative AI.

This course aims to equip you with the crucial skill of prompt engineering and working with Generative AI for efficient task automation, focusing on the art of communicating effectively and systematically with Large Language models to automate tasks and optimise the outcomes.

This course begins with a comprehensive introduction to Generative AI, laying the groundwork for an in-depth exploration of prompt engineering. It starts by building a solid understanding of neural networks, deep learning principles, and fundamental Natural Language Processing (NLP) concepts, essential for effectively engaging with AI models. As the course progresses, it delves into Generative AI models, including Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). However, the heart of the curriculum lies in comprehending the ground-breaking capabilities of the Transformer architecture, the foundation of modern Large Language Models. You’ll develop the theoretical framework and the practical skills necessary to craft creative tailored prompts for Large Language Models and develop automation pipelines, enabling you to exploit these powerful tools for problem-solving and automation across various industries.

Through practical exercises, you’ll explore topics like the design and development of Large Language Models from scratch, working with state-of-the-art Large Language Models, formulating systematic prompts for optimal interaction with Large Language Models, Working with APIs, task automation, end-to-end data pipelining, as well as content creation. The course also highlights the ethical considerations of using Large Language Models, emphasizing the need for responsible prompt engineering to mitigate biases and maintain fairness in AI applications.

The module will provide an introduction to emerging techniques allowing data scientists and practitioners to study and investigate nonlinear time series. For example, we will learn how to diagnose whether highly fluctuating and random appearing data are most likely driven by random or deterministic dynamic forces, we will analyse how to detect causality, long-range correlations, define and detect stability in complex systems.

This module will offer a collection of tools designed to dive deep down into underlying structures of data, allowing future data scientists to detect whether stochastic or deterministic dynamics most likely drive observed complexity. In other words, this module will teach how to become 'data detectives ‘accumulating hard empirical evidence supporting their modelling approach.

The teaching material is organised in a way to support graduate students to be familiar with the basic concept of nonlinear dynamics and become operational in nonlinear time-series analysis (NLTS) paving the way for NLTS to be adopted as conventional empirical toolbox in their future studies or professional practice. The approach to the subject will be highly interactive, with hands-on computer experiments in R code directing the students through NLTS methods and helping them understand the underlying logic.

This module helps to prepare a postgraduate student for undertaking research. Its purpose is twofold: to introduce you to the discipline of research and, at the same time, to help lay the essential foundations for a dissertation of MSc quality.

Student interests, time constraints and other practical considerations usually set, and limit, the topic, the research approach and study, and the selection of suitable method(s) appropriate for a MSc dissertation.

The module therefore includes: - a consideration of research design issues; - an introduction to research skills; - an evaluation of alternative research methods. Topics covered include, for example, research planning and design, alternative research methods, use of the Internet, research analysis and effective time management. During the module, you’ll define an area of study that could or will form the basis of your research project.

You’ll be expected to undertake an appropriate and critical review of the available literature and other information relevant to the proposed project. If a laboratory-based project is envisaged, you will need to give consideration to the instrumentation needed, provide experimental design criteria, at least for the first stages of the proposed work, and take account of all health and safety regulations and appropriate ethical considerations.

If necessary, you will need to prepare a formal application for ethical approval of your work.