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Course Details

Master of Science in Computing (Multimodal Human Language Technology)

Course code: BN530 Entry Route into programme:
  • Second Class Honours grade 2 (GPA 2.5 or equivalent) in a Bachelor of Science (Honours) in Computing (level 8) or equivalent. Graduates of BN509 should refer to the minimum entry requirements as detailed below
Duration: 2 years (4 Semesters)
NFQ level: 9

Fee: €2,000 per year ** Graduates of BN509 or an equivalent course may be eligible for a fee waiver**

Schedule: Tuesday and Thursday 6.00pm -10.00pm                            Programme commences Week of 22/01/18
Award title: Master of Science
Credits for Full Award: 90 @ NFQ Level 9
Awarding Body: ITB


Information Technology is one of Ireland’s most important economic sectors, employing approximately 5% of the Irish workforce and accounting for a third of Irish exports (€32 billion per annum). The Dublin region has a significant concentration of IT multinationals similar to Silicon Valley and through its successes in the IT sector, Ireland has built an enviable international reputation as a technology center that is leveraged to attract investment to other sectors.

Against the background of growth in demand for IT courses and the needs of the business community, the Institute of Technology Blanchardstown (ITB) have designed a new level 9 Master of Science in Computing degree where we expect to be a significant contributor to the needs of the economy and the regional business and social community.

The 90 credits NFQ level 9 masters programme will be made up of a taught component and a research project component.  The emphasis of the programme is on applied skills with modules designed with the collaboration of industry and academia and where possible, modules will include content from industry certifications.  The research component will be focused on real-world business problems and where possible, the research project will be completed during an internship with a company. 

The courses are suitable for both entrants to a new discipline that require a broader range of taught modules to familiarize themselves with the skills and knowledge of the discipline and for specialist employees who want to up-skill in their specialist areas and require a narrow range of taught modules and more emphasis on the research skills project of the degree.

With our new degree structure, we seek to be flexible and responsive to industry needs while still maintaining a high standard of academic excellence.

Students will be required to take a minimum of 3 taught modules worth 10 credits each, giving a total of 30 credits.  Students then have the option of two different paths to complete the 90 credits required for this master’s degree.  One option is to take three more taught modules and a 30-credit research project.  This path would suit students who are new to the discipline and require more taught modules and a smaller research project.

The other path is to take a 60 credit research project module route.  This path would suit students who have a background in the discipline and who wish to dedicate more time to the research element of the masters and develop their research capabilities.

The MSc Research Project is a significant amount of work as it comprises from one-third to two-thirds of the credits for the masters.  The research project will have at it core an applied component and incorporate the collecting and analyzing data for improving decision making purposes.

Human Language Technology (HLT) refers to a rapidly evolving interdisciplinary field concerned with one of our most pervasive commodities: human language (both spoken and written) and its interaction with computers. In the past decade it has become an increasingly central component in the field of computer science, as it has become increasingly prevalent in our lives.

Human Language Technology is concerned with human language, as it appears in emails, web pages, tweets, product descriptions, newspaper stories, social media, and scientific articles, in thousands of languages worldwide. Successful human language technology applications have become part of our everyday experience.

The Human Language technology professional will have expertise in the domain areas of computer science, computer engineering and linguistics and combining expertise in these domain area provides us with a myriad of new and exciting innovations from conversational agents/avatars and automatic speech recognition on user devices to Internet search and machine translation of the world’s languages.

Who Should Apply for the MSc in Computing at ITB?

The MSc in Computing programme is of particular value to holders of a primary degree in computing, IT, or equivalent, working as IT professionals. It is also of value to individuals with a computing degree background who wish to develop their career towards working within a research-oriented environment at a postgraduate level.


Minimum Entry Requirements

The minimum entry requirement for standard entrants to the Master of Science in Computing is 2nd Class Honours Grade 2 (GPA 2.5 or equivalent), in a Bachelor of Science in Computing (level 8) course, or equivalent.

Students progressing through the Higher Diploma in Science in Computing (BN509) who wish to enter the M.Sc. in Computing must attain a 2nd Class Honours Grade 2 (GPA 2.5) on the course.

In the event of a student not attaining this standard level , students must achieve an acceptable standard for progression by other means approved by QQI.

The acceptance of candidates with third class honours degrees and appropriate work experience and industrial certification on this course will be allowed provided there is evidence that the candidate can cope with the learning objectives of the course.

Candidates may be interviewed to assess their suitability to undertake the level of work required and to assess their commitment to succeding on the MSc in Computing Programme.

Careers Opportunities

As of now, most of the world's top IT companies have significant bases in Ireland (including, for example, IBM’s R&D centre and the European headquarters of Google, Microsoft, Facebook all based in Dublin) and a large portion of their language technology services are conducted in Europe. According to LT-Innovate (2014), with a growth in excess of 10%, year on year, language technology is the new frontier in IT and is a domain in which European researchers and companies have been pioneers for decades.


Graduates of this new specialist Masters programme will have an exciting range of career opportunities available to them, from machine translation, data analytics and speech processing, to e-learning, Internet search, and human-computer interaction.

Schedule for Delivery
Classes commence during the week of Monday 22nd of January. A full academic calendar is available online at

Tuesday 6:00pm – 10:00pm
Thursday 6:00pm – 10:00pm

Number of places on the course:
The programme has 40 places available for the intake commencing January 2018



Year 1 Semester 1
  • Natural Language Processing
  • XML Ontologies for Corpus Building
  • Linguistics for Human Language Technology
Year 1 Semester 2
  • Data Structures & Algorithms for NLP
  • Digital Corpus Linguistics
  • Interactive Speech Processing
Year 2 Semester 3
  • Avatar Technology
  • Programming for NLP
  • Intelligent Software Agents
  • Machine Translation Paradigms
Year 2 Semester 4
  • Research Skills & Ethics
  • MSc Research Project


 Students must complete 6 modules and a thesis. All Modules are 10 ECTS Credits. The MSc Research Project is 30 ECTS Credits.

The order of Module Delivery may differ from the listing above.

An award of ‘Postgraduate Diploma in Science in Computing’ may be granted if learners leave this programme having completed 6 taught modules and attained 60 credits with a minimum GPA of 2.00.


Natural Language Processing

Module Aims:

  • To give practical exposure to the student to the main research areas in that part of computer science concerned with the processing of written and spoken human language within software- Natural Language Processing. Concentrate on the practical application of advanced structures and algorithmic techniques in solving language problems in software.
  • Provide the student with the ability to undertake parsing and generation of human language in software.
  • Provide students with the ability to apply Natural Language Processing techniques, software solutions and computational intelligence in the areas of internet development, search engines, machine translation, speech processing and human computer interfaces.

Learning Outcomes:

  1. Select the appropriate techniques, software algorithms and structures for use in solving practical language related software-engineering problems.
  2. Understand how human languages are constructed, apply appropriate techniques to tokenise, parse and generate human language in software, and to build grammars in software.
  3. Understand how internet based solutions that process language are constructed, i.e. search engine software.
  4. Apply computational approaches to language and speech processing in software.
  5. Critically analyse technical research articles and papers related to natural language processing.


XML Ontologies for Corpus Building

Module aims:

  • Students will gain an in-depth knowledge of ontologies and ontology languages. Students will have the ability to use tools to build and store ontologies.
  • Students will understand the underlying ideas of Semantic Web and its layered architecture, and will become familiar with its main technologies.

Learning outcomes:

  1. Use ontology tools and apply methodologies.
  2. Use ontology description languages.
  3. Use ontology querying languages.
  4. Use structured web documents.
  5. Develop ontologies and knowledge bases.


Linguistics for Human Language Technology

Module aims:

  • To provide students with in-depth skills and knowledge of the role of language structure and grammatical description in natural language processing as a human language technology.
  • To give students the skills necessary to apply a wide range of techniques for linguistic analysis at the morpho-syntax and semantics interface.
  • To provide students with the necessary theoretical skills and practical applications framework knowledge for research in linguistics for human language technology.
  • To instill an in-depth understanding of the importance and value of quality in empirical analysis to human language technology and NLP.

Learning outcomes:

  1. Articulate the role and place of language structure and grammatical description in a HLT / NLP strategy.
  2. Apply best practice research methods using authentic natural language data in linguistic analysis.
  3. Undertake research-motivated empirical analysis of human languages.
  4. Apply advanced knowledge and skills in the applications of the functional models of language and grammars in HLT / NLP.


Data Structure & Algorithms for NLP

Module aims:

  • To provide students with in-depth skills and knowledge of the role of advanced data structures in software engineering, in particular their design, analysis and correctness.
  • To give students the skills necessary a wide range of algorithmic techniques for software development.
  • To provide students with the necessary theoretical and practical applications framework for software engineering for natural language applications.
  • To instill an in-depth appreciation of the importance of quality in software development.

Learning outcomes:

  1. Research, select and apply the appropriate data structures and algorithms to use in solving practical and complex problems in software design for natural language applications.
  2. Evaluate and differentiate between stacks, queues, trees and graphs and other data structures and apply the ‘O notation’ in evaluating algorithms performance and time / space complexity.
  3. Research and develop solutions associated with data structure choices in the provision of software solutions in natural language applications.
  4. Research and develop solutions associated with algorithm choices in the provision of software solutions in natural language applications.
  5. Explore and integrate the use of the graph and tree type data structures in text-based information retrieval.
  6. Research, design, implement and test Abstract Data Types including stacks, queues, linked lists, trees and graphs.
  7. Implement and evaluate the performance of classical software algorithms including sorting algorithms, searching algorithms, tree and graph algorithms.


Digital Corpus Linguistics

Module aims:

  • To provide students with in-depth knowledge and skills of the role of digital corpora in natural language processing as a human language technology, in particular their design, analysis and correctness.
  • To give students the skills necessary a wide range of techniques for digital corpus development and natural language applications.
  • To provide students with the necessary theoretical and practical applications framework for research using a digital corpus for natural language applications.
  • To instill an in-depth appreciation of the importance of quality in the digital corpus development.

Learning outcomes:

  1. Articulate the role and place of the digital corpus in an NLP strategy.
  2. Apply best practice research methods in using a digital corpus of natural language through appropriate corpus markup and annotation.
  3. Undertake research-motivated analysis of text using a corpus.
  4. Use and design text-oriented software programs.
  5. Critically differentiate current issues in digital corpora creation and use.


Interactive Speech Processing

Module aims:

  • To introduce learners to automated human speech processing techniques that can be applied to a variety of applications such as speaker recognition, speech to text and text to speech systems. Structure of speech signals will be analysed and the challenges of the technology addressed.

Learning outcomes:

  1. Analyse human speech signals in terms of component waveform frequencies.
  2. Create a synthesised human speech signal.
  3. Demonstrate the challenges and best practices in speech to text systems.
  4. Demonstrate how speech processing may be applied to biometric identification.
  5. Explain how prosody in human speech can be used as an additional element for more efficient automation of signals.


Speech Processing Technology

Module Aims:

  • The learner will gain knowledge and experience of working with speech signal processing technology and learn about about the key signal processing techniques underpinning modern speech technology applications and how they impact the performance of the system.

Learning Outcomes:

  1. Identify, describe and apply signal processing techniques to speech processing applications and speech feature extraction.
  2. Compare and select suitable features set to be employed for a specified speech application.
  3. Describe and evaluate the components of automatic speech recognition systems.
  4. Describe and evaluate the components of text-to-speech synthesis systems.
  5. Describe and critique emerging applications of speech technology.


Avatar Technology

Module Aims:

  • The learner will investigate the concept of, and applications for, embodied agents. Topics addressed in this module include: motion capture, 3D character design and animation, HCI for AI and Sign Language synthesis.

Learning Outcomes:

  1. By way of the literature in the field; discuss the state-of-the-art in real-time and batch rendering avatar technologies as they are used in the context of HCI and HLT.
  2. Analyse and critically evaluate current issues in the field of embodied agents and avatar technologies. These might include, but are not limited to: accessibility, social interaction, human emotion synthesis and collaborative virtual environments.
  3. Describe the various methods used to animate avatars. Including: motion capture, synthesis and traditional frame-by-frame animation.
  4. Demonstrate various state of the art tools used to direct 3D character movement.
  5. Identify various taxonomies used in data driven avatar movement such as XML, VHML and SiGML.


Programming for NLP

Module Aims:

  • To provide students with in-depth knowledge and skills of the role of programming skills and strategies for NLP contexts.
  • To give students the skills necessary a wide range of NLP related algorithmic techniques for software development.
  • To provide students with the necessary theoretical and practical applications framework for programming natural language applications.
  • To provide students with in-depth knowledge and skills regarding the complex challenges of processing human languages in software.
  • To instill an in-depth appreciation of the importance of quality in software development.

Learning Outcomes:

  1. Develop programmes that can manipulate and analyse human language data.
  2. Apply core knowledge concepts from NLP and linguistics to analyse and describe human language.
  3. Apply data structures and algorithms in NLP.
  4. Store complex language data in standard formats.
  5. Evaluate the performance and effectiveness of NLP techniques.


Intelligent Software Agents

Module Aims:

  • Critically analyse the principles of agent program design using object-orientation and present the essential software algorithms used to develop agents that reason, model, and learn to adapt to the world around them in an environment that is dynamic, continuous, non-deterministic and potentially inaccessible.
  • Apply Agent algorithms and techniques to practical "real-world" distributed computing applications using the Internet and TCPIP socket programming.
  • Develop an intelligent agent architecture and use this to construct several agent-enhanced programs.

Learning Outcomes:

  1. Identify, create and deploy appropriate algorithms and techniques to create intelligent agents in software for use over the Internet.
  2. Build an intelligent agent framework to support object-oriented, network facing applications for use over the Internet.
  3. Apply solutions involving complex search and state-based perspectives of an application domain.
  4. Apply appropriate formal knowledge representation techniques based on the Standard Interchange Formats.
  5. Build a domain specific knowledge base and create a reasoning system over that base.
  6. Design appropriate inter-agent communication architecture.


Machine Translation Paradigms

Module Aims:

  • The learner will demonstrate a range of standard and specialized research or equivalent tools and techniques of inquiry with regard to machine translation paradigms. In doing so, learners will have the opportunity to implement and evaluate various open source machine translation solutions while gaining an understanding of the underlying theory.

Learning Outcomes:

  1. Demonstrate an awareness of state-of-the-art research and tools/techniques of inquiry with regard to machine translation paradigms.
  2. Describe the challenges that face the translation industry and the various paradigms of machine translation that attempt to address those challenges.
  3. Experiment with various open source machine translation implementations.
  4. Discriminate between automated evaluation solutions for machine translation.


Research Skills & Ethics

Module Aims:

  • Essential skills for engaging in research covering all stages of research from finding a research question to research methodologies, to writing dissertation and publishing.

Learning Outcomes:

  1. Expertly describe the different stages that make up a research project.
  2. Demonstrate how to write a research proposal.
  3. Critical review of data collection methods.
  4. Appraisal of research methodologies.
  5. Demonstrate the different types of dissemination for project reports and results.
  6. Appraisal of ethical approaches in research.


MSc Research Project :


30 Credits:

  • Independent research project to give learners the experience of developing an individual computing project at postgraduate level. Learners will demonstrate their responsibility for substantial independent working and a full project from problem specification through to implementation and evaluation.

Learning Outcomes:

  1. Investigate various approaches to research enquiry and develop a research proposal.
  2. Write a literature review for selected research questions by reporting on relevant existing research demonstrating appropriate academic citation and referencing.
  3. Demonstrate the purposes and procedures involved in data gathering techniques, data analysis and discussion of results.
  4. Dissemination of results through presentations and research thesis documentation.

60 Credits:

  • This research module builds on your existing postgraduate experience to enable you to complete your training as a researcher. You’ll develop a research proposal by identifying and explaining a research problem relevant to your MSc. Your research will involve a literature review, data collection, data analysis, results and conclusions. You will then communicate the outcome of your research through presentations and dissertation report.


Learning Outcomes:

  1. Demonstrate self-direction and originality in planning tasks and solving problems during a research project.
  2. Prepare a comprehensive review or critical evaluation of existing research literature and/or professional guidance on a specific topic.
  3. Evaluate the research findings in relation to applicable techniques, theoretical limitations and experimental or design considerations.
  4. Analyse data showing originality in its interpretation in relation to scientific literature.
  5. Synthesise appropriate conclusions and findings through knowledge and systematic understanding of the research process and any limitations of the work.
  6. Communicate the outcomes of research to professional standards through a dissertation, poster and oral presentation.


How to Apply ?

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What our students say...

Geri Wickham

"I have done a few software modules in ITB, so I knew the focus would be different if I came here. The practical experience here really helped me. Until I started doing Software, I was a complete technophobe. I was working as an engineer, but now I do more quality and IT. I work in a very small company, everybody has to do bits of everything, and I learnt a lot here, and it gave me great confidence."

Geri Wickham,
Computing Student

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