Module Descriptors

Artificial Intelligence

This module provides the basis for the understanding and use of knowledge representation and reasoning techniques in AI systems in general, and knowledge-based systems in particular. The module covers notions of representation and the relationship between representation and that which is represented, along with issues of the resources required to manipulate such representations. The focus is on different logic-based representation languages and proof search using logical calculi, but other approaches are also discussed.

Data Mining and Exploration

Data mining is about analysing, interpreting, visualizing and exploiting the data that is captured scientific and commercial environments. This module provides students with an opportunity to gain an in depth understanding of the Data Science theories and issues related to mining and exploring data, ranging from statistical summaries, to visualization, to classification and clustering. Practical case studies will be used for illustration.

Machine Learning

This module explores supervised learning (predicting outputs from inputs), unsupervised learning (discovering structure without output “teacher signal”), and reinforcement learning (learning through interaction with an environment). Students will compare different learning algorithms in depth. Unlike the Data Mining Exploration module, which focused on applying algorithms to large real-world datasets, this module emphasizes the technical and mathematical details of details of the studied algorithms.

Introduction to Computational Linguistics

This module introduces the basic theory and practice of computational approaches to natural language processing. The module covers the following topics:  introduction to programming in Python & NLTK, tokenization, part-of-speech tagging, context-free grammars for natural language, evaluating a natural language processing system, parsing techniques, information extraction, etc. The module also provides an introductory insight into the state of current research in Computational Linguistics, including AI and Data Science techniques.

Recent Trends and Issues in AI

This module critically examines recent trends, developments, and challenges in AI and Informatics. As technology rapidly evolves, students will explore cutting-edge topics such as Intelligent Information Systems, AI, Data Science, Cybersecurity, and Quantum Computing, along with related ethical issues.

Through lectures and independent research, students will engage with current discourse and gain a solid understanding of emerging informatics trends and their impact on society, business, and policy.

Speech and Language Processing

The module provides students with a comprehensive understanding of the fundamental principles, techniques, and applications in the field of natural language processing (NLP) and speech processing. The module covers theoretical concepts as well as practical implementations, enabling students to develop skills necessary for designing and implementing various speech and language processing systems. The module will cover also how deep learning and neural network are being applied and implemented to the processing of speech and natural language.

IoT Applications in AI and Security

The module explores the transformative potential of the Internet of Things (IoT) and the critical importance of ensuring security in IoT ecosystems. It provides a comprehensive understanding of IoT concepts, architectures, and protocols, along with insights into various domains where IoT technologies are applied, such as smart cities, healthcare, education, agriculture, and industrial systems. The module focuses on various aspects of IoT security, including privacy, ethical considerations, and legal implications. It explores the critical security challenges within IoT ecosystems, such as vulnerabilities and threats, and discusses countermeasures to safeguard IoT devices, networks, and data.

Big Data Technologies and Applications

This module provides students with an opportunity to gain an in depth understanding of Big Data technologies. The module will cover topics ranging from how data is stored (in both relational and graph databases) to frameworks such as Map Reduce to cloud computing. Students will also learn about the main challenges faced when dealing with big data. Practical case studies will be used for illustration.

Dissertation

This module involves the design, execution, and completion of a research dissertation, fulfilling part of the MSc requirements. Students explore an approved topic in AI or data science, applying research methods and demonstrating independent inquiry.

Assessment includes a written dissertation detailing the research conducted (25,000–35,000 words, excluding references/appendices) and an oral dissertation presentation (viva) —a 15–20 minutes presentation followed by a 30–40 minutes Q&A with an academic panel.