Module Descriptors

Informatics Research Methods

This module provides a comprehensive introduction to the methodologies and skills required to conduct informatics research, especially AI and Data Science research. It focuses on understanding the nature of informatics and informatics research, formulating research questions, and developing hypotheses. This module will delve into defining research objectives and scope and selecting suitable research methods and tools. It also provides a significant focus on research evaluation and assessment, such as criteria for assessing informatics research, validity, reliability, and generalizability of research findings, and assessing research impact and significance.

Knowledge Representation and Reasoning

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.

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 course also provides an introductory insight into the state of current research in Computational Linguistics, including AI and Data Science techniques.

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.

Knowledge Management

The module teaches the principles and technologies of knowledge management. A case study approach, as and where appropriate, will be adopted in introducing the course contents. The module covers the fundamental concepts in the study of knowledge and its creation, representation, dissemination, use and re-use, and management. The focus is on methods, techniques, and tools for computer support of knowledge management, knowledge acquisition, and how to apply a knowledge management system using one of the knowledge-based system tools.

Machine Learning

The module discusses supervised learning (which is concerned with learning to predict an output, from given inputs), reinforcement learning (which is concerned about learning from interacting with an environment), unsupervised learning, where we wish to discover the structure in a set of patterns; there is no output “teacher signal”. We will compare different learning algorithms, and unlike Data Mining Exploration module where the focus was on the applying algorithms to large real-world data sets, in this course we will get to the technical and mathematical details of the studied algorithms.

Recent Informatics Trends and Issues

This module explores and critically evaluates the recent trends, developments, and challenges in the field of Informatics. As the technology landscape continues to evolve rapidly, it is imperative for students to stay updated with the cutting-edge advancements and ethical issues arising in the areas like Data Science, Cybersecurity, Intelligent Information Systems, and Quantum Computing, among others. Students will be exposed to the contemporary discourse of Informatics and be given the opportunity to delve deeper into specific areas of interest. Through an engaging combination of lectures and independent research, students will gain a robust understanding of the current informatics trends and their implications for 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 covers also how machine learning, deep learning and Neural Network are being applied and implemented to the processing of speech and natural language.

IoT Applications 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 course 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 Data Science case studies will be used for illustration.

Enterprise Architecture and IT Governance

The module provides a comprehensive understanding of the principles and practices involved in aligning IT systems with business objectives and ensuring effective IT governance within organisations. Students will gain knowledge of various frameworks and methodologies used in enterprise architecture, and learn how to assess, design, and implement IT systems that support organisational goals. This module also covers IT governance strategies, risk management, compliance, and the ethical considerations associated with IT decision-making. Students will develop the skills necessary to lead enterprise architecture initiatives, collaborate with stakeholders, and make informed decisions that optimise IT investments and drive organizational success.

Dissertation

This module concentrates on the development, design and completion of student research dissertation as a partial fulfilment of master’s requirement. Dissertations are intended to give students an opportunity to focus on an aspect of informatics, in particular AI or data science, and to investigate it in more detail; the topic will have to be pre-approved by the Programme Coordinator or potential Supervisor. This will help them develop skill as independent researchers. Students will also apply some of the techniques needed to conduct research and develop knowledge in the subject area of the programme of study.

The assessment of the dissertation is comprised of two parts: an oral dissertation viva and a written dissertation detailing the research conducted. The oral dissertation presentation (viva) is typically a 15-20 minutes presentation followed by 30-40 minutes of Q&A in front of a relevant jury. The written dissertation’s word count is typically in the range of 25K-35K. This is a words-equivalent word count and excludes references and appendices.