The student will develop key practical skills and a foundation in Machine Learning techniques including data analysis and artificial intelligence and their application on specific problems in Machine Learning, such as, prediction, classification, recommendation and optimization.

1. Critically appraise the performance of a learning system.
2. Summarise and evaluate relevant research into machine learning.
3. Analyse and evaluate the main approaches to machine learning and show similarities and differences between different approaches.
4. Develop and appraise appropriate machine learning classification, optimisation and regression problems.
5. Implement machine learning algorithms to classification, regression and optimisation problems specifically with Big Data.
6. Examine and appraise the ethical and governance considerations associated with machine learning.

Artificial Intelligence (AI) is one of the most popular areas in computer science and engineering. AI deals with intelligent behaviour, learning, and adaptation in machines, robots and body-less computer programs. This module serves as an introduction to the broad field of AI. The course provides a basic introduction to classical AI as well as non-classical AI. It introduces logic and automated reasoning, probabilistic reasoning and reasoning under uncertainty. It also covers Biologically inspired AI, the Ethics of AI, explainability of AI, and the links between AI and Machine Learning.

1. Give an overview of the field of artificial intelligence, its background, history, fundamental issues, challenges and main directions .
2. I nterpret and formulate knowledge representations in the form of logic expressions
3. Critique concepts, methods and th eories of embodied cognition in artificial systems
4. Critique concepts, methods and theories of biologically inspired intelligence systems
5. Increase AI interpretability using explainable AI approaches
6. Appraise agents and their applications
7. Increase critical thinking skills, analytical problem-solving skills and awareness of artificial intelligence -related ethics

To provide the student with a significant level of comprehension both of the theoretical concepts underpinning databases for Big Data and also how to critically appraise the appropriateness for industry.

1. Defend the selection of an appropriate scripting language for the analysis of data
2. Compose a complex application appropriate to analysis and solution of a large scale problem solving and data analysis requirement
3. Identify, explore and make informed judgement relating to the design, modelling and implementation of data analytics systems paying particular attention to legal and ethical issues
4. Reflect upon their personal professional practice demonstrating appropriate and effective communication and organisational skills in the development of an application as part of a team
5. Analyse complex problems resultant from the use of large-scale data sources for data analysis and defend appropriate approaches to solving the business problem
6. Analyse complex and large-scale big data problems using graphs
7. Apply research, information gathering, critical analysis, design and implementation techniques appropriately and effectively
8. Design and implement standard implementation techniques for the communication of analysis in a business appropriate manner.

To provide the student with a significant level of comprehension and aptitude for both the theoretical concepts underpinning Big Data analytics using a mathematical approach and critical appraisal of the outputs from Big Data analytics as they relate to business applications.

1. Critically analyse and manipulate large amounts of data.
2. Evaluate statistics derived from large data sets.
3. Understand the nature of statistical inference.
4. Use a statistical package to produce useful visualisations of large data sets.
5. Appraise, review and critically formulate a mathematically-based analysis of large data sets.
6. Determine non-trivial trends/patterns in large data sets.

To provide the student with a significant level of comprehension both of the theoretical concepts underpinning large scale data architecture for Big Data systems. The student will investigate different data management strategies and have knowledge and skills to design a data management framework. The student will build a data pipeline which incorporates data ingestion, integration, cataloguing and storage techniques using current technologies to create a self-service environment for data users.

1. Discuss data management strategies and roles.
2. Analyse selected data frameworks designs for Big Data architectures.
3. Investigate techniques and technologies available for specific data pipeline stages.
4. Design and implement a data architecture and configuration for a modern data pipeline.
5. Manage and orchestrate a complete data workflow using appropriate tools and techniques.
6. Evaluate best practices for data management systems.

The goal of Artificial Intelligence (AI) is to develop and implement machines that behave as though they are intelligent that have the capability to solve problems that are normally associated with the higher intellectual processing capabilities of humans. This module provides the student with a detailed understanding of AI models used for computer vision and natural language processing. The student will gain a comprehensive understanding of the components of text analytics, natural language processing (NLP), and large language model (LLM) systems, and gain an appreciation of the mechanisms required for computer vision algorithm implementation.

1. Differentiate between Text Analytics and NLP systems.
2. Critique, analyse and examine key components required to successfully build a text analytics system.
3. Design and implement a text analytics and NLP system to create meaningful output suited to a particular use case.
4. Analyse the various elements of a Computer Vision system.
5. Design, develop and implement algorithmic techniques to build a Computer Vision system.
6. Critically examine case studies to identify a Computer Vision problem and implement a suitable solution.