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 investigates the professional, legal and ethical dimensions when developing such capabilities in businesses and society. The module discusses the aforementioned issues from the perspective of raising awareness and developing Responsible Computing (RC) professionals that will see tomorrow’s development of Digital Society.
Furthermore, this module will aspire to examine cases in various application areas of AI (such as Data Mining, Information Retrieval, Recommendation systems, Natural Language Processing (NLP), social network analysis and text mining) with a view of how these could be developed with RC in mind as well as observing the Ethics Guidelines for Trustworthy AI published by EU.

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.

For the dissertation, the students work individually on a project under the supervision of a lecturer. In the project students will be developing a software solution to a real problem using the skills and knowledge they acquired from all the modules, and from outside sources and materials they will investigate at the duration of the project. At the end students develop and practice research skills that will help them further develop in the future as AI and Data Science experts.

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.