This module is necessary to the curriculum since it will alert the students to potential implications and the great impact of AI in the society at large. IT in general is a discipline that has lately huge impact in society, given the proliferation of computing in our everyday life and the ubiquitous of computing. As such all undergraduate courses have Professional aspects module as compulsory in the curriculum. Given the recent rise of AI, and its application in medicine, in cars, in weapons, in financial, in manufacturing, to name a few, it is very apparent that tomorrow’s practitioners need to be made aware of the great responsibility that they carry on their hands. Whilst the other modules , such as Machine Learning and Neural Networks, teach them the mechanics of AI, this module aspires to teach them the implications and how careful they need to be when these mechanics are put into operations and the need for reflection and holistic view of what is to be created.

Artificial Intelligence (AI) is the area of Computer Science with the ultimate goal to build intelligent machines, i.e. machines that exhibit human-like behaviour when solving complex problems. This module provides an in-depth introduction to explainable classic or knowledge-driven Artificial Intelligence. The three main pillars under which topics are presented are single intelligent agents, multi-agent systems and biology-inspired agents. The topics include the two main areas of classic AI presented from both theoretical and practical perspective, i.e. Knowledge Representation (logic and its variants, state-space representation, semantic and knowledge graphs, frames and ontologies, rules etc.) and Reasoning (resolution and refutation, search, knowledge retrieval, types of reasoning, rule-extraction in classic machine learning, backward and forward chaining etc.). The module will explicitly refer to agent models for practical reasoning and coordination, communication, collaboration and negotiation between agents. Nature-inspired agents will give an opportunity to touch upon biological reactive agents and will focus on emotional reasoning and simulation of collective emotional intelligence.

This module explores a range of the most relevant topics that pertain to contemporary analysis practices, technologies, and tools for Big Data environments. Main aspects and challenges of Big Data will be addressed by introducing relevant algorithms and practices. Additionally, this course provides a detailed description and hands-on experience to cutting- edge open-source software such as Apache Hadoop, Apache Kafka, etc. Students will be introduced and gain awareness, in a gradual manner, to the concepts, algorithms and techniques that cover key Big Data topics and will thoroughly use Apache Spark and Python for the coursework.

This module introduces students to the fundamental elements, concepts and techniques involved in Data Science applications. Students initially acquire a good understanding of probabilities, statistics and Linear algebra concepts required in Data Science. Students will also gain experience in cleaning, transformation, analysis of data as well as visualisation of data. The module has a practical dimension through the use of an appropriate programming language.

This module aims to help students acquire skills and knowledge for project-based software development in the industry. The module provides fundamental knowledge on agile processes and continuous software quality management practices as well as hands-on experience on industry toolkits for continuous integration, deployment and delivery of software artifacts.

Deep learning is a hot topic that has found multiple areas of application in the industry and business. Deep learning is the extension of Neural Networks (NN) that includes some new developments in training algorithms and uses the versatility of the computing power and data storage of the cloud. The module briefly introduces neural networks, explains how they work, how they are trained, and how they are deployed. Furthermore, it discusses the recent developments in training algorithms, NN structures, and cloud deployment, to conclude with the practical application of Artificial Intelligence solutions that we now call Deep Learning.

The dissertation provides students with the opportunity to undertake and carry out a project under the supervision of one member of staff. The project is identified and prepared as part of the Research Methods and Dissertation Preparation module. Academic staff members offer initial ideas for research project work, but students are encouraged to develop their own ideas, and they are expected to carry out the research themselves. The completed project is presented as a typed dissertation of not less than 14,000 words.

The purpose of this module is to provide students with the opportunity to integrate and apply the skills and the knowledge they have acquired so far in their studies to a realistic problem. Students are exposed to the processes involved in the team-based development of software through real projects that are provided by companies from the software industry.

Machine Learning (ML) is the part of Artificial Intelligence (AI) that studies how computers build experience and autonomously learn from data. The module will follow the standard machine learning taxonomy for organising problems and applying solution techniques, and will provide a thorough grounding in the theory and application of machine learning.

Through this module, students develop their research skills and get prepared for working on their MSc dissertation. With the guidance of their supervisors, students are introduced to the research topics and techniques that are commonly employed in software engineering research.  Students are exposed to and exercise the principles and practices of report writing, literature reviewing, and research designs and approaches.