Artificial Intelligence (with Advanced Practice) MSc

Teesside University

You gain a deep insight into the business applications of artificial intelligence (AI) and data science (DA). You explore a range of AI and DS applications such as chatbots, virtual assistants, medical diagnosis, biometric recognition, personalisation, fraud detection and autonomous machines, and analyse both the risks and opportunities of applying AI and DS techniques in these areas.

You gain the foundational knowledge to study a wide range of AI applications and solutions, and are introduced to logic-based knowledge representation, reasoning, problem solving and algorithms, planning and AI applications.

You undertake a major, in-depth, individual study in an aspect of your course. Normally computing master's projects are drawn from commercial, industrial or research-based problem areas. The project involves you in researching and investigating aspects of your area of study and then producing a major deliverable, for example software package or tool, design, web-site and research findings. You also critically evaluate your major deliverable, including obtaining third party evaluation where appropriate. The major deliverable(s) are presented via a poster display, and also via a product demonstration or a conference-type presentation of the research and findings. The research, project process and evaluation is reported via a paper in the style of a specified academic conference or journal paper. The written report, the major deliverable and your presentation of the product are assessed. The project management process affords supported opportunities for goal setting, reflection and critical evaluation of achievement.

You focus on the fundamentals of tackling decisions of increasing difficulty in technology, health and business decision, and gain an understanding about the need for, and the effectiveness of, computerised methods for supporting decisions. This includes classifications, data mining and knowledge management-based decision methods with examples of various application domains. You will be provided with the opportunity to implement simple computerised decision support systems applied to specific real-life problems. The process and practices develop your ability to build simple versions of decision support systems and familiarity with full-scale versions of decision support systems for various application domains.

Machine learning is a subfield of computer science concerned with computational techniques rather than performing explicit programmed instructions. You build a model from a task based on observations in order to make predictions about unseen data. Such techniques are useful when the desired output is known but an algorithm is unknown, or when a system needs to adapt to unforeseen circumstances. You explore statistics and probability theory as the fundamental task is to make inferences from data samples. The contribution from other areas of computer science is also essential for efficient task representation, learning algorithms, and inferences procedures. You gain exposure to a breadth of tasks and techniques in machine learning. Assessment is an in course assessment (100%).

You gain the knowledge and skills to understand the research process in computing and digital media, and the necessary skills to undertake your masters project. You learn how to use and critically evaluate previous academic research, and to generate good evidence material to justify their professional practice. This involves you learning about different research strategies and data generation methods and how they fit into the development lifecycle and the evaluation of the user experience, the use of the academic research literature, and research ethics. Assessment involves you preparing a research proposal which can form the basis of your master's project.

You develop necessary knowledge and practical understanding of the main statistical techniques. You explore quantitative and qualitative data analysis techniques, reflecting scientific and social science methods. You focus on correlation testing, regression, data categories, normalization - the tools needed, rather than the philosophical approaches. You understand how to apply valid techniques and interpret the results in preparation for experimental work. Your assessment is a single ICA based around a number of case studies that require you to identify the correct data analysis and modelling processes.