Computer Science MSc Postgraduate Programme By Ulster University |TopUniversities

Programme overview

Main Subject

Computer Science and Information Systems

Degree

MSc

Study Level

Masters

Study Mode

On Campus

The MSc programme consists of two compulsory taught modules (totaling 40 credits), four optional taught modules (totaling 80 credits) from a wide range of topics, and a substantial independent Masters Project (60 credits). As part of the programme, students will be required to use various programming languages, including Python and R. The two compulsory modules are: Scalable Advanced Software Solutions This module aims to explore a range of modern development and deployment concepts in the context of scalable and high-performance computing services. Within this module concepts such as containerisation, Continuous Integration, Continuous Delivery, cloud architectures, scalable solutions and infrastructure will be explored. Additionally, advanced programming/development concepts facilitating high performance solution development will be examined. Data Science and Machine Learning This module provides an overview of Data Science process/pipeline. It provides systematic understanding of mathematical and statistical knowledge for explorable data analysis (EDA) and to understand the foundations of supervised and unsupervised machine learning algorithms, and with the practical programming skills to apply them to real world datasets. The module discusses the constraints that needs to be considered when designing, implementing, evaluating and visualising solutions to real-world complex problems. Example optional modules are Cyber Security Cyber security, which has an impact on national security, infrastructure, and the global economy, is one of today's most pressing issues. Due to the enormous digital threat, cyber security knowledge is among the most in-demand globally. This course examines recent advancements in cyber security theory and practice. To enable critical cyber security decision-making, the students will develop the fundamental and advanced aspects of cyber security in terms of theory, practice, policy, and security standards. They will also learn about the threats to current and emerging systems and networks and how to effectively counter them in accordance with information security management standards. The students will learn about the social, legal, and ethical issues surrounding cyber security. Deep Learning and Its Application The module will introduce the fundamentals of deep learning, construction of neural networks and theory of developing successful deep learning algorithms. Students will learn state of the art convolutional neural networks, recurrent neural networks, loss functions and optimisation process along with development tools, and apply them to develop solutions for applications of computer vision and natural language processing. IoT Networks and Protocols The Internet of Things (IoT) describes the interconnectivity of uniquely identifiable devices embedded in the environment through internet protocols and infrastructure. The module will evaluate and critically appraise IoT networking concepts, models, standards, protocols and practical skills. It will address Sustainability Development Goals, inform on the evolving IoT use cases, and appraise related issues such as the impact of IoT on a citizen’s privacy. Robotics & AI This module provides an overview of smart robotics and AI. It is designed to provide students with a strong foundation through the core topics and the key technologies of robotics and AI while providing hands-on experience on programming smart robots in the labs. The module will explore practically coding AI techniques for Robotics and the focus is given to design and implement smart robots exhibiting AI behaviours. Knowledge Engineering This module will cover modern topics in a classical field of artificial intelligence, including knowledge representation and reasoning (deductive and inductive), and their effective utilisation in e.g. decision making, automated reasoning and formal verification, and semantic web. Students will gain deep understanding of key concepts and principles, and gain practical skills in critically evaluating and effectively building knowledge-based applications. Intelligence Engineering and Infrastructure The aim of this module is to educate students on best practices for engineering, deploying, testing and orchestration intelligence across modern computing. This will include aspects of Machine learning, federated operation of activities, data engineering, production of tailored computational artefacts (such as models which are tailored for a range of device type), production pipelines, automated testing and automated deployment. Emerging and Advanced Topics in AI This module will cover cutting-edge topics in the field of artificial intelligence, including recent advances in AI theory, algorithms and applications, as well as issues such as privacy, fairness and ethics in artificial intelligence. In doing so a number of examples of advanced AI systems and applications are reviewed. Students will gain deep understanding of key concepts, principles, and challenges, and gain practical skills in critically evaluating and effectively building AI-based applications. The module will also help students develop their skills in independent learning, research skills, writing, as well as practical skills in using software to reproduce results from the literature. Note: The optional modules that are offered may vary from year to year as the course develops. The running of optional modules are subject to minimal viable number of students taking each module per Academic Year.

Programme overview

Main Subject

Computer Science and Information Systems

Degree

MSc

Study Level

Masters

Study Mode

On Campus

The MSc programme consists of two compulsory taught modules (totaling 40 credits), four optional taught modules (totaling 80 credits) from a wide range of topics, and a substantial independent Masters Project (60 credits). As part of the programme, students will be required to use various programming languages, including Python and R. The two compulsory modules are: Scalable Advanced Software Solutions This module aims to explore a range of modern development and deployment concepts in the context of scalable and high-performance computing services. Within this module concepts such as containerisation, Continuous Integration, Continuous Delivery, cloud architectures, scalable solutions and infrastructure will be explored. Additionally, advanced programming/development concepts facilitating high performance solution development will be examined. Data Science and Machine Learning This module provides an overview of Data Science process/pipeline. It provides systematic understanding of mathematical and statistical knowledge for explorable data analysis (EDA) and to understand the foundations of supervised and unsupervised machine learning algorithms, and with the practical programming skills to apply them to real world datasets. The module discusses the constraints that needs to be considered when designing, implementing, evaluating and visualising solutions to real-world complex problems. Example optional modules are Cyber Security Cyber security, which has an impact on national security, infrastructure, and the global economy, is one of today's most pressing issues. Due to the enormous digital threat, cyber security knowledge is among the most in-demand globally. This course examines recent advancements in cyber security theory and practice. To enable critical cyber security decision-making, the students will develop the fundamental and advanced aspects of cyber security in terms of theory, practice, policy, and security standards. They will also learn about the threats to current and emerging systems and networks and how to effectively counter them in accordance with information security management standards. The students will learn about the social, legal, and ethical issues surrounding cyber security. Deep Learning and Its Application The module will introduce the fundamentals of deep learning, construction of neural networks and theory of developing successful deep learning algorithms. Students will learn state of the art convolutional neural networks, recurrent neural networks, loss functions and optimisation process along with development tools, and apply them to develop solutions for applications of computer vision and natural language processing. IoT Networks and Protocols The Internet of Things (IoT) describes the interconnectivity of uniquely identifiable devices embedded in the environment through internet protocols and infrastructure. The module will evaluate and critically appraise IoT networking concepts, models, standards, protocols and practical skills. It will address Sustainability Development Goals, inform on the evolving IoT use cases, and appraise related issues such as the impact of IoT on a citizen’s privacy. Robotics & AI This module provides an overview of smart robotics and AI. It is designed to provide students with a strong foundation through the core topics and the key technologies of robotics and AI while providing hands-on experience on programming smart robots in the labs. The module will explore practically coding AI techniques for Robotics and the focus is given to design and implement smart robots exhibiting AI behaviours. Knowledge Engineering This module will cover modern topics in a classical field of artificial intelligence, including knowledge representation and reasoning (deductive and inductive), and their effective utilisation in e.g. decision making, automated reasoning and formal verification, and semantic web. Students will gain deep understanding of key concepts and principles, and gain practical skills in critically evaluating and effectively building knowledge-based applications. Intelligence Engineering and Infrastructure The aim of this module is to educate students on best practices for engineering, deploying, testing and orchestration intelligence across modern computing. This will include aspects of Machine learning, federated operation of activities, data engineering, production of tailored computational artefacts (such as models which are tailored for a range of device type), production pipelines, automated testing and automated deployment. Emerging and Advanced Topics in AI This module will cover cutting-edge topics in the field of artificial intelligence, including recent advances in AI theory, algorithms and applications, as well as issues such as privacy, fairness and ethics in artificial intelligence. In doing so a number of examples of advanced AI systems and applications are reviewed. Students will gain deep understanding of key concepts, principles, and challenges, and gain practical skills in critically evaluating and effectively building AI-based applications. The module will also help students develop their skills in independent learning, research skills, writing, as well as practical skills in using software to reproduce results from the literature. Note: The optional modules that are offered may vary from year to year as the course develops. The running of optional modules are subject to minimal viable number of students taking each module per Academic Year.

Admission Requirements

We recognise a range of qualifications for admission to our courses. In addition to the specific entry conditions for this course you must also meet the University’s General Entrance Requirements. Entry Requirements Applicants must: (a) have gained (i) a second class honours degree or better, in the subject areas of Computer Science, Software Engineering, Electronic Engineering, Electrical Engineering, Mathematics, Physics or closely related discipline, from a university of the United Kingdom or the Republic of Ireland, or from a recognised national awarding body, or from an institution of another country which has been recognised as being of an equivalent standard; or (ii) an equivalent standard (normally 50%) in a Graduate Diploma, Graduate Certificate, Postgraduate Certificate or Postgraduate Diploma or an approved alternative qualification excluding Conversion courses; and the qualification must be in the subject areas of Computer Science, Software Engineering, Electronic Engineering, Electrical Engineering, Mathematics, Physics or closely related discipline. and (b) provide evidence of competence in written and spoken English (GCSE grade C or equivalent). For applicants whose first language is not English the minimum English language requirement is an Academic IELTS 6.0 with no band score less than 5.5, Trinity ISE: Pass at level III or equivalent English language tests comparable to IELTS equivalent score. In exceptional circumstances, as an alternative to (a) (i) or (a) (ii) and/or (b), where an individual has substantial and significant experiential learning, a portfolio of written evidence demonstrating the meeting of graduate qualities (including subject-specific outcomes, as determined by the Course Committee) may be considered as an alternative entrance route. Evidence used to demonstrate graduate qualities may not be used for exemption against modules within the programme. English Language Requirements English language requirements for international applicants The minimum requirement for this course is Academic IELTS 6.0 with no band score less than 5.5. Trinity ISE: Pass at level III also meets this requirement for Tier 4 visa purposes. Ulster recognises a number of other English language tests and comparable IELTS equivalent scores. English language requirements Your country Exemptions and transferability The entry requirements facilitate accreditation of prior learning.

Jan
Sep

Scholarships

Selecting the right scholarship can be a daunting process. With countless options available, students often find themselves overwhelmed and confused. The decision can be especially stressful for those facing financial constraints or pursuing specific academic or career goals.

To help students navigate this challenging process, we recommend the following articles:

More programmes from the university

Postgrad Programmes 1641