This module provides a comprehensive grounding in Machine Learning (ML) algorithms and their application in a multi-disciplinary range of domains.

The course will cover the 4 areas of ML: Supervised Learning (Classification) Unsupervised Learning (Clustering) Regression (Predictive modelling) Dimensionality reduction

The module will also cover practical aspects including dataset pre-processing and will describe techniques for algorithms selection and parameterisation. Additionally, the process for training the model and reporting model performance will be detailed.

1. Navigate and utilise machine learning algorithms from state-of-the-artlibraries

2. Identify problems that can be modelled using machine learning techniques.

3. Determine the class of problem and the model required, based upon the available data and desired outcome.

4. Pre-process the data for use by machine learning libraries

5. Select an appropriate algorithm and identify a training strategy and parameter set to develop a working model.

6. Analyse and present results on the performance of the model using best-practice techniques.

1. Describe current state-of-art research and solutions for embedded systems

2. Describe current state-of-art research and solutions for Pervasive sensing

3. Design embedded systems that utilise low level assemble language, appreciating the strengths and weaknesses of this type of approach

4. Design embedded systems that utiliseLinux, appreciating the strengths and weaknesses of OS-based embedded solutions

5. Develop embedded systems application with monitor or connect to the local environment usingsensors and external hardware devices.

Building dependable software systems is key to the success of modern enterprise. This module aims to teach students how to develop and maintain large scale software projects which are testable, maintainable, flexible, and dependable. Students will be provided a grounding in Test-driven development approaches which will inform the internal structure of their projects. The module will explore the importance of quality assurance (with the development process) and quality control (assessment of the end product). The module will be highly practical in its delivery and aims to be easily deployable in a students future workplace practice.

1. Have a systematic understanding of Software Quality processes & policies

2. Discriminate between appropriate and inappropriate implementations of Software Quality mechanisms within their areas of expertise/function

3. Lead and initiate the implementation of Software Testing and other quality assurance mechanisms within broad scope software projects

4. Appraise, and adjust,an existing approach to Software Testing & Quality assurance within a suitable industry context

5. Implement, at scale, code which is aligned to the tenets of Testability

This module introduces key principles of cybersecurity and secure programming. Learners will explore how to identify and respond to security threats, automate tasks to support cybersecurity, and develop more resilient software. Emphasis is placed on secure coding practices, threat analysis, and the use of practical tools and techniques to evaluate and improve system security.

1. Understand cybersecurity concepts, including threat modelling, common attack vectors, and defensive strategies.
2. Analyse and evaluate areas in cybersecurity that can be automated with software using secure programming principles.
3. Investigate vulnerabilities and assess security posture through ethical hacking methodologies, frameworks, and penetration testing.
4. Apply machine learning techniques to cybersecurity challenges, such as malware classification, anomaly detection, or vulnerability prediction.
5. Implement secure programming techniques to mitigate common software vulnerabilities, such as input validation, output encoding, secure error handling, and safe API use.
6. Analyse applications using static and dynamic analysis tools to identify insecure patterns, malware artefacts, and indicators of compromise.

The aim of this module is to give students a deeper and broader view of the topic, through consideration of the elements of constructing database system software, and by examining the variety of systems and applications currently in use and being researched.

1. Demonstrate knowledge and understanding of the issues involved in developing database management software.

2. Demonstrate knowledge and understanding ofthe reasons for the variety of database types now available.

3. Design databases using relational and post relational architecture patterns.
4. Develop databases using relational and post relational architecture patterns.
5. Emerging Database Technologies – Understand how modern applications and technologies integrate with various types of databases.
6. Query Optimisation and Performance Tuning – Demonstrate and develop efficient SQL queries to fine tune and optimise database management systems.
7. Advanced database design and modelling – designing and developing complex data models for enterprise environments.

This module provides advanced knowledge of modern techniques for natural language processing and sequence analysis. It also presents insights into how artificial intelligence and algorithms can be applied to text analytics.

1. Think in abstract ways about complex unstructured text-based computing problems.

2. Apply current theoretical and programmatic techniques to textapplications and problems.

3. Understand the challenges and limitations of current approaches to text and sequence analysis.

4. Applying abstract algorithmic approaches to complex, unstructured and highly noisytext-based problems.

5. Analyselarge volumes of unstructured text-based data and extractmeaningful patterns and insights.

This module provides learners with advanced knowledge of statistical computing and its application to complex real-world problems. The module focuses on providing both the theoretical foundations necessary for advancing in this field with a set of practical programming skills.

1. Evaluate and critique the applicability of differentstatistical modelling techniques to solve complex computational problems.

2. Understand the challenges and limitations of current statistical approaches to the analysis of large data sets.

3. Design and implement novel computational solutions using statistical models and methods.

4. Apply simulation techniques to practical problems involving statistical inference and numerical analysis.

Software architecture and development processes have been transformed by the revolution in cloud computing. This has lead to the emergence of a new set of cloud native patterns and techniques which can enable organisations to take advantage of modern cloud environments to build and run loosely-coupled systems which are scalable and resilient. Combined with robust automation, they allow engineers to make changes frequently and predictably with minimal friction.

This module will explore the concepts, practices and tools of this cloud native landscape and equip students with the knowledge and skills needed to engage meaningfully in cloud transformation in industry.

1. Contrast cloud native practices with traditional models

2. Critically appraise cloud application architectures and communication patterns

3. Implement modern strategies for building and deploying cloud applications

4. Automate cloud infrastructure management

5. Evaluate approaches for ensuring the security and resilience of cloud-based systems

The module will introduce the student to a variety of aspects of digital forensics with a focus on file system forensics, operating systems forensics and network forensics. The module will explore the complexity and operation of file system forensics, examining methods used to recover data. The module will also examine the monitoring of network traffic for the purposes of forensic analysis.

1. Demonstrate a critical understanding of the core theories, concepts and principles of computer and network forensics.

2. Analyse data hardware and software storage formats for a variety of digital devices and computers.

3. Use digital forensics tools and techniques to extract and analyse data from digital storage devices and networks.

4. Use a programming language to extract forensic data, investigate forensic artefacts, and generate evidence reports.

5. Analyse and present findings from an electronic discovery process and investigation.

This is a course on research methods specific to research in computer science and informatics. Completion of this module prior to commencing the Masters Project is compulsory for all students on the M.Sc. in Computing. The course is assessed by a portfolio of course work (55%), and a project proposal and presentation (45%).

This course is designed to provide students with an introduction to the methods used to carry out a postgraduate research project in computing and related disciplines. It is designed for students from a wide variety of backgrounds and aims to help them to develop critical thinking and to learn research techniques. In particular, it will provide them with the skills that they will need to undertake their own research on the M.Sc. in Computing programme.

1. Compare the use of different methods of research in computing.

2. Critically evaluate and review different types of publication.

3. Formulate a research question and select an appropriate methodology to investigate it.

4. Design and carry out a significant research project in an area of computing.

5. Present and report on research in a manner acceptable to the computing research community.

The Masters Project must be substantial and of a suitable technical level in line with the overall standards required by QQI for Masters level computing degree programmes. It is envisaged that the majority of part-time participants for this degree will work on a project related to their current work, or areas of research relevant to their work environment. This will help foster closer co-operation between the department and industry and should make it easier for part-time participants to allocate sufficient time to their project work.

For each project, an academic supervisor will be assigned from the programme board to oversee the research. In the case of students completing their project in situ at work, the supervisor may consult with the participant's employer or direct manager to determine how much of the project work can reasonably be done as a part of the participant's normal duties. A flexible approach will be adopted to allow participants access to departmental computing facilities where this need arises.

The project will normally be undertaken after the six required subject modules and the Research Methods module are completed. In exceptional circumstances, where applicable, it may be commenced in situ at work, after the successful completion of a minimum of three 10-credit modules and the Research Methods module. While the project should be completed during a fixed time interval, part-time students may apply for an extension to the period, subject to approval of the course board.

1. Demonstrate the application of appropriate research methodologies and techniques within the domain of computing and software systems.

2. Apply modern research methods appropriate to applied computing research problems / questions.

3. Demonstrate an awareness of the present state of the art in a specialist area of computing including the ability to evaluate the established literature base in that subject area.

4. Independently acquire and assess relevant knowledge that is contextually appropriate and specific to an applied area of computing research.

5. Apply research and critical thinking skills to a challenging computer-based problem.

6. Design and implement a computing solution that requires significant preliminary research.

7. Communicate to peers, both written and verbally, on their applied research / dissertation topic, in an articulate, convincing and informed fashion.