Applied Artificial Intelligence (with optional Placement/Project year) MSc

1. Projects which will involve the application of methods and equipment introduced in taught modules, will be based on subjects agreed in principle with the Postgraduate Dissertation Coordinator and potential supervisors.
2. The research dissertation may be University-based or carried out in the employer’s workplace, or through a work placement where a local organisation has a direct role in facilitating the project.

To afford students the opportunity to experience the complete life-cycle of a successful and significant research-based project

To provide real-world experience of meeting the requirements of academic and professional standards, including high-level writing and referencing skills.

To demonstrate to peers and to current and potential employers the student’s ability to carry out good quality academic research, in a particular field, which is relevant to their programme of study. This may involve the application of existing research within a novel context.

To include:

• Time management, library skills and literature search
• Evaluation of information sources
• Critical analysis of information
• Ethical issues in science, technology and engineering research (including intellectual property and plagiarism)
• Writing for research: styles and rules for presentation (including referencing standards)
• Choosing a research area and evaluating source material
• Hypothesis formation
• Research approaches and methodologies
• Design and application of questionnaires & interviews
• Quantitative and statistical tools for researchers (e.g. R, Python, SPSS)

• To clarify the distinctions between undergraduate and postgraduate level work and expectations
• To increase students' experience in order to conduct a professional study and to use sampling procedures and analysing techniques.
• To improve students' appreciation of time management and how to conduct a literature search
• To reinforce students' research skills
• To consolidate students' appreciation of professional issues such as copyright and ethics

• Evolution of Robotics
• Microcontrollers: Arduino and Raspberry Pi
• Computer Vision
• Agents and Multi-Agent Systems
• Machine learning and robotics

• To introduce the concept of artificial intelligence (AI) and to evaluate its role in the development of robotics.
• To introduce theoretical approaches to the development of intelligent robots.
• To undertake practical tasks to demonstrate how AI techniques can be implemented for robotics.
• To analyse methods for designing and deploying robotic systems.
• To critically evaluate the ways in which intelligent robots can be used in real world situations

This module investigates tools and techniques to extract, transform and load (ETL) data into a data warehouse for the purpose of online analytical processing (OLAP). Students will be guided through step-by-step demonstrations showing them how to perform the ETL process using a suitable tool, such as Python or R. Tools such as SQL and Excel will be used to demonstrate extracting data for visualisation and analysis, e.g., building a data cube. Additionally, the module will investigate alternative approaches to data warehousing, e.g., the Hadoop ecosystem.

This module introduces concepts of data science as a discipline and develops students skills in the areas tacking the manipulation of data such as loading, transforming and storing data.

This module investigates different types of machine learning algorithms to find patterns in data. Each algorithm will be discussed in theory and practice, discussing: its data pre-processing requirements, pseudo-code, and evaluation metrics, e.g., Dunn index for clustering. Detailed demonstrations will show how to apply these algorithms on data using specified libraries in either Python or R. Students will be required to investigate the merits of each algorithm for various types of data in both theory and practice.

Students in this module will learn how to use, apply and develop machine learning tools for data science applications.

This module provides students with an exploration into the realm of Artificial Intelligence (AI) with a focus on understanding, evaluating, and proposing novel approaches within the field. Students studying this module, will explore complex problem solving using AI models and develop a deep understanding of state-of-the-art techniques through research and analysis. The module focuses on developing students problem solving using the Python programming language and applying learned skills towards solving real-world AI problems using Python and its specialist libraries e.g. Matplotlib and Pytorch. At the end of the module students will be able to critically assess AI models/solutions and be able to propose new approaches and apply learning to the solving of new problems.

This module aims to provide students with a conceptual and critical understanding of best AI approaches and implementation techniques and how to apply Python solutions to solving real-world AI problems. Students will be able to evaluate models/solutions and propose/develop new solutions to solving novel and emerging problems. 

This module will be broken into four distinct sections:

• The first section of the module will ask the question of 'what is Artificial Intelligence' and what specifically differentiates it from other types of machine learning. Does the advent of AI pose ethical issues and what challenges may we face? 
• In the second section of the module, we will explore the ethical implications of AI, what are the ethical implications of its implementation and development, how do we address problems around algorithmic bias
  etc. 
• The third section of module will explore some of the use cases of AI and current state-of-the-art development practices and methodologies. We will also look at the potential societal impact of incorporating aspects of AI into aspects of individuals day-to-day lives. 
• The fourth and final section will look at how we govern the development of AI and the ethical and moral principles we need to adhere to going forward.

This module aims to provide students with an awareness and critical understanding around the ethical, social and regulatory issues around the development of artificial intelligence and related areas.