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The module is partly based on the first three sections of “Artificial Intelligence: A Modern Approach” (3rd edition) by Russell and Norvig. The indicative content from the book is as follows: Introduction: What is AI? History of AI and the state of the art. Agents : An introduction to agents, their behaviors and structure Searching: Problem solving by searching, heuristics, local search and optimisation and adversarial search Constraint satisfaction problems: defining and solving CSPs Logic: Propositional logic, first-order logic, knowledge representation In addition to this the module will feature introductions to other AI techniques including neural networks, machine learning and nature inspired methods.

The module aims to present a balanced approach to the subject area by addressing both the theory and practice of Business Intelligence and reporting in enterprise systems. The aim of this module is to develop a deep understanding of enterprise information systems and their role in business processes alongside practical skills in business intelligence reporting and dashboard design. It includes:- Evaluation of the impact that Enterprise Information Systems can have within organisation business processes. - As an assessment of the impact that they have on the efficiency and effectiveness of organisations and supply chains. - Fundamentals of business intelligence, such as data warehousing and data mining. - The role of performance dashboards in performance management and measurement in organisations. - The sourcing and extraction of data and the analysis and development of intelligence. - Understanding of current legislation on data privacy and ethical issues relevant to business intelligence in organisations.The module will use a range of tools to give students an appreciation of the nature of large scale integrated information systems and to practice data analytical techniques.

In this module you will:- develop an awareness of the use of modern computational intelligence techniques;- develop an understanding of a number of techniques such as evolutionary algorithms, neural networks, co-evolution, classifier systems and fuzzy logic;- gain practical experience of implementing and evaluating computational intelligence algorithms.

The aim of this module is to enable you to develop a deep understanding of the fundamentals of data analytics, and to give you opportunities to practise a set of popular data analytical tools. Topics covered include:*Data Pre-processing – data quality, data cleaning, data preparation*Data Analytics – techniques of analysing data, such as classification, association, clustering and visualisation, including a variety of machine learning methods that are widely used in data mining* Post processing – data visualisation, interpretation, evaluationThis module will use tools such as OpenRefine, Weka and Tableau for standard and structured data The Benchmark Statement for Computing specifies the range of skills and knowledge that should be incorporated in computing courses. This module encompasses cognitive skills in Computational Thinking, Modelling and Methods and Tools and practical skills in deployment and use of tools and critical evaluation in addition to providing useful generic skills for employment.

This module will explore and develop data management and processing solutions that will work on dirty, complex, real-world data. This module will examine the key concepts of data warehousing, data cleaning, and data processing in the context of business requirements and focus on how to combine these steps into a coherent data processing pipeline.First, modern tools and techniques in data management will be examined, with the emphasis on good practice and professional approaches of storing and handling data. Next, the module will examine ways of cleaning noisy real-world data in order to make it suitable for data processing. Finally, data processing and collation techniques such as Machine or Deep Learning will be applied to the data to extract structure and elicit comprehension of the data. Throughout the module, advantages and disadvantages of using local and cloud approaches will be explored, alongside discussing common parallel approaches to facilitate faster solutions.In short, the goal of this module is to allow students to understand a data processing pipeline from raw data to final delivery. It will cover:• Data warehousing and storage techniques• Data cleaning techniques• A discussion of cloud approaches• Data processing and collation techniques• An introduction to parallel data pipeline approaches

The work for this module comprises the completion of an individual research project. Each student is assigned a personal Supervisor, and an Internal Examiner who monitors progress and feedback, inputs advice, examines the dissertation and takes the lead at the viva. There are two preliminary deliverables prior to the submission of the final dissertation: (1) Project proposal (2) Initial Report including time plan and dissertation outline

Conversational AI has attracted increasing attention in academia and industry in the past five years. There have been over 100 companies working on voice-AI or Conversational AI-based products, including the world’s most valuable brands (e.g. Apple, Google, Amazon, Huawei, etc.) and start-ups (e.g. Emotech, Alana). On the other hand, there are global competitions organised by Amazon, which focus on state-of-the-art technologies in diverse conversational AI tasks, e.g. social conversation and task-oriented conversation. This module can help students learn about conversational AI, related NLP and ML/DL methodologies, and state-of-the-art research topics. It also can provide students with the experience of team-level project work on realistic human daily problems, which offers a good insight into academic and industrial perspectives. The module covers the following topics: • Data types and formats: numerical and time series, graph, textual, unstructured, • Data sources and interfaces: open data, APIs, social media, web-based • NoSQL databases such as document (MongoDB), graph and key-value pair • Techniques for dealing with large data sets, including Map Reduce • Developing Data-Driven Applications in Python The Benchmark Statement for Computing specifies the range of skills and knowledge that should be incorporated in computing courses. This module encompasses cognitive skills in Computational Thinking, Modelling and Methods and Tools, Requirements Analysis and practical skills in specification, development and testing and the deployment and use of tools and critical evaluation, and providing useful generic skills for employment.