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Computer Science-Artificial Intelligence
Master of Science (Computer Science-Artificial Intelligence)
College of Science and Engineering, School of Computer Science- Title of Award
- Master of Science
- Average Intake
- 40
- Delivery
- On Campus
- NFQ
- Level 9
- Award Type
- Major
- Next Intake
- September 2025
- Duration
- 1 year, full-time
- ECTS Weighting
- 90
Why Choose This Course?
Course Information
Who is this course for?
This programme is aimed at graduates with a primary qualification in Computer Science or related subject area. It is not a conversion course, but expects students to be already at a very high technical standard with regard to their Computer Science education. This MSc is targeted at high-performing graduates of Level 8 computer science programmes, or Level 8 science/engineering programmes that offer sufficient training in computing. The minimum academic requirement for entry to the programme is a First Class Honours (or equivalent) from a recognised university or third-level college. However, a good Second Class Honours (or equivalent) can be deemed sufficient on the recommendation of the Programme Director.
What will I study?
The MSc is a 1 year, fulltime, 90-ECTS course with three main elements: core modules (45 ECTS), optional (elective) modules (15 ECTS), and a substantial capstone thesis project (30 ECTS).
Core modules include: Programming and Tools for AI; Introduction to Natural Language Processing, Machine Learning; Deep Learning; Research Topics in AI; Information Retrieval; Artificial Intelligence and Ethics; Agents, Multi-Agent Systems & Reinforcement Learning.
Optional modules include: Advanced Topics in Natural Language Processing; Knowledge Representation; Optimisation; Knowledge Graphs; System Modelling & Simulation; Tools and Techniques for Large Scale Data Analytics; Web & Network Science.
From Semester 2 onwards, students work on individual projects and submit them in August. Projects may have a research or applied research focus.
Core modules:
- Programming and Tools for AI
- Introduction to Natural Language Processing
- Machine Learning
- Deep Learning
- Research Topics in AI
- Information Retrieval
- Artificial Intelligence and Ethics
- Agents, Multi-Agent Systems & Reinforcement Learning
Optional Modules may include:
- Advanced Topics in Natural Language Processing
- Knowledge Representation; Optimisation
- Knowledge Graphs
- System Modelling & Simulation
- Tools and Techniques for Large Scale Data Analytics; Web & Network Science.
Curriculum Information
Curriculum information relates to the current academic year (in most cases).Course and module offerings and details may be subject to change.
Glossary of Terms
- Credits
- You must earn a defined number of credits (aka ECTS) to complete each year of your course. You do this by taking all of its required modules as well as the correct number of optional modules to obtain that year's total number of credits.
- Module
- An examinable portion of a subject or course, for which you attend lectures and/or tutorials and carry out assignments. E.g. Algebra and Calculus could be modules within the subject Mathematics. Each module has a unique module code eg. MA140.
- Optional
- A module you may choose to study.
- Required
- A module that you must study if you choose this course (or subject).
- Semester
- Most courses have 2 semesters (aka terms) per year.
Year 1 (90 Credits)
RequiredCT5142: Artificial Intelligence and Ethics
CT5142: Artificial Intelligence and Ethics
Semester 1 | Credits: 5
Artifical intelligence technologies have evolved dramatically in recent years, impacting on many areas of human life. Societal responses to these developments have ranged from enthusiastic optimism to deep suspicion. The module will explore prominent ethical issues arising in relation to the design, use and societal impact of Artificial Intelligence. Topics addressed in the module include Embedded values, ethics by design and trustworthiness of AI; Privacy, consent, dark patterns and contextual integrity; Algorithmic fairness, bias and algorithmic governance; Assistance and surveillance; Datafication, surveillance capitalism and monopoly; AI and the workplace; generative AI; autonomous artificial agents and responsibility; AI and the environment; AI risk and safety.
(Language of instruction: English)
Learning Outcomes
- Demonstrate competence in using specialist ethical concepts
- Identify and summarise important ethical concerns related to the design, use and societal impact of Artificial Intelligence.
- Apply relevant theoretical models from the ethical, legal and social science literature to identified ethical concerns regarding AI.
- Critically analyse strengths and weaknesses of different positions from the ethical, legal and social science literature on ethical concerns related to the design, use and societal impact of Artifical Intelligence.
- Demonstrate the ability to communicate ethical concerns coherently and concisely for professional and lay audiences.
Assessments
- Continuous Assessment (100%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
Reading List
- "The Oxford Handbook of Ethics of AI" by Markus Dubber, Frank Pasquale, Sunit Das (eds)
Publisher: OUP - "Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor" by Virginia Eubanks
Publisher: St Martin's Press - "Ethics of Artificial Intelligence" by Matthew Liao (ed.)
Publisher: OUP - "Robot Ethics 2.0: From Autonomous Cars to Artificial Intelligence" by Patrick Lin, Keith Abney, Ryan Jenkins (Editors)
Publisher: Oxford University Press - "Privacy in Context: Technology, Policy, and the Integrity of Social Life" by Helen Nissenbaum
Publisher: Stanford University Press - "The Oxford Handbook of Digital Ethics" by Carissa Veliz (ed.)
Publisher: OUP - "Privacy as Trust: Information Privacy for an Information Age" by Ari Waldman
Publisher: Cambridge University Press - "The Age of Surveillance Capitalism" by Shoshanna Zuboff
Publisher: Profile Books
Note: Module offerings and details may be subject to change.
RequiredCT5132: Programming and Tools for AI
CT5132: Programming and Tools for AI
Semester 1 | Credits: 5
This module is about programming and computational tools required for artificial intelligence. It uses the Python language as the main vehicle, but focusses on conceptual material rather than just the language
itself. It moves fast through introductory Python workings. It covers several important Python libraries in detail, especially for numerical computing, machine learning, plotting, graphs. It discusses approaches to building re-usable, high quality code but not software engineering per se. It also visits some extra topics such as version control and introduction to the R language for statistics.
The module is core for the University of Galway MSc in Artificial Intelligence (MScAI) Part-time (online) and Full-time (classroom). The syllabus and assessment will be the same for both.
(Language of instruction: English)
Learning Outcomes
- Read and write simple Python programs, e.g. for data munging, with a high degree of comfort.
- Use R for simple statistics and data exploration.
- Use numerical Python libraries for manipulation, input/output, visualisation of numerical data using Numpy array types.
- Use essential tools for AI, including libraries for data gathering, numerical computing, machine learning, combinatorial programming, and modelling networks.
- Plan/design a program using any of the above facilities; test it; document it; execute it locally or in the cloud as appropriate.
Assessments
- Written Assessment (50%)
- Continuous Assessment (50%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
Reading List
- "Think Python" by Allen Downey
- "A Whirlwind Tour of Python" by Jake Vanderplas
Note: Module offerings and details may be subject to change.
RequiredCT5120: Introduction to Natural Language Processing
CT5120: Introduction to Natural Language Processing
Semester 1 | Credits: 5
Natural Language Processing (NLP) is concerned with the automatic analysis, interpretation and annotation of textual data. Applications of NLP are in the extraction of information from text, linking text to databases or other structured knowledge, classification, summarization, translation and generation of text, etc. This module introduces students to the field of NLP, including linguistic, statistical and machine learning foundations, primary challenges and approaches to the syntactic and semantic analysis of textual data, and applications in summarization, chatbot development, knowledge extraction and opinion mining. The course ends with a discussion of ethical aspects in NLP.
(Language of instruction: English)
Learning Outcomes
- Ability to explain the various levels of linguistic structure relevant to NLP.
- Ability to use standard algorithms for basic NLP analysis
- Gain practical knowledge of and experience in the use of NLP toolkits
- Ability to explain a selection of theoretical principles behind core NLP applications.
- Ability to apply NLP algorithms, toolkits and applications to tasks in AI, Data Analytics and other related application areas.
Assessments
- Written Assessment (50%)
- Continuous Assessment (50%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
The above information outlines module CT5120: "Introduction to Natural Language Processing" and is valid from 2024 onwards.Note: Module offerings and details may be subject to change.
RequiredCT5129: Artificial Intelligence Project
CT5129: Artificial Intelligence Project
15 months long | Credits: 30
This capstone research project tests the students ability to carry out
in depth analysis, problem-solving and reporting of an AI problem.
(Language of instruction: English)
Learning Outcomes
- Identify and research an Artificial Intelligence (AI) problem
- Identify, describe, and synthesize the state-of-the-art approaches to the problem
- Devise and implement a solution to the problem which has novelty
- Carry out an appropriate (e.g., experimental) evaluation on the solution
- Write up the problem, state-of-the-art, methodology and implementation, (experimental) evaluation, results and their discussion, and conclusion as a thesis.
Assessments
- Research (100%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
The above information outlines module CT5129: "Artificial Intelligence Project" and is valid from 2024 onwards.Note: Module offerings and details may be subject to change.
RequiredCT4100: Information Retrieval
CT4100: Information Retrieval
Semester 1 | Credits: 5
The course introduces some of the main theories and techniques in the domain of information retrieval.
(Language of instruction: English)
Learning Outcomes
- Explain the main models used in information retrieval.
- Explain the factors involved in designing and analysing weighting schemes
- Be able to choose suitable data structures and algorithms for building IR systems
- Explain the main ideas and approaches used in web search
- Explain the main ideas and approaches used in recommender systems
- Explain the concepts in applying learning mechanisms in information retrieval
Assessments
- Written Assessment (70%)
- Continuous Assessment (30%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
The above information outlines module CT4100: "Information Retrieval " and is valid from 2024 onwards.Note: Module offerings and details may be subject to change.
RequiredCT5165: Principles of Machine Learning
CT5165: Principles of Machine Learning
Semester 1 | Credits: 5
Machine Learning is concerned with algorithms that improve their performance over time, as they are exposed to new data. This module introduces learners to the different categories of machine learning tasks and provides in-depth coverage of important algorithms for tackling them. Its focus is on the theory underlying ML algorithms. In addition, the learners gain experience of implementing algorithms from scratch, as well as using ML software tools to select and apply these algorithms in applications, and they evaluate and interpret the results.
Topics include:
1. Overview of Machine Learning & Major Categories of Task
2. Supervised Learning Principles and Information-Based Learning
3. Similarity-Based Learning
4. Evaluating Classifier Performance, Practical Advice, and Some Machine Learning Tools
5. Linear Regression in One and Multiple Variables
6. Linear Classifiers with Hard and Soft Thresholds
7. Probabilistic Machine Learning
(Language of instruction: English)
Learning Outcomes
- Define Machine Learning and explain what major categories of learning tasks entail
- Demonstrate how to apply the machine learning and data mining process to practical problems
- Explain and apply algorithms including decision tree learning, instance-based learning, probabilistic learning, linear regression, logistic regression, and others
- Given a dataset and task to be addressed, select, apply and evaluate appropriate algorithms, and interpret the results
- Discuss ethical issues and emerging trends in machine learning.
Assessments
- Written Assessment (70%)
- Continuous Assessment (30%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
The above information outlines module CT5165: "Principles of Machine Learning" and is valid from 2024 onwards.Note: Module offerings and details may be subject to change.
RequiredCT5135: Research Topics in AI
CT5135: Research Topics in AI
Semester 2 | Credits: 5
Research Topics in AI will provide an in-depth coverage of two or three active research areas in the field of AI. For each topic the module provides an overview of research in the area and the implications from real-world implementations.
(Language of instruction: English)
Learning Outcomes
- Gain both a wide and a deep knowledge of the topic(s) in the current offering of the module.
- Improve their skills at navigating through, and critically examining, the scientific literature on the selected topic(s).
- Demonstrate the use of techniques from the selected topic(s) using real-world datasets and tools.
- Gain practical knowledge from invited speakers from academia and industry regarding state of the art tools and applications.
- Familiarise to challenges in relation to Smart Energy and Grids, and be able to demonstrate some applications in this area using appropriate codebase or datasets.
- Outline challenges relating to business and big data, with attention to issues such as fraud detection etc.
- Learn challenges relating to Smart Cities, and demonstrate the use of Machine Learning through the use of Intelligent Transportation Networks.
- Experience challenges related to business and big data, with attention to issues such as health care, fraud detection, autonomous vehicles, etc.
Assessments
- Continuous Assessment (100%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
The above information outlines module CT5135: "Research Topics in AI" and is valid from 2024 onwards.Note: Module offerings and details may be subject to change.
RequiredCT5100: Data Visualisation
CT5100: Data Visualisation
Semester 2 | Credits: 5
Visualisation is a fundamental technique in presenting the properties of data and the results and evaluation of data analytical processes. For a data visualisation to be successful the analyst needs to have considered the properties of the data, the information to be communicated, the mode of visualisation delivery and the expectations of the audience. This module takes a practical approach to introducing learners to the strengths and weaknesses of human perception, and the use of best practices to represent complex and large data stories using visual primitives. The module demonstrates the role of visualisation in exploratory data analysis and its fundamental role in explaining data analytical outcomes. The module emphasises the need to communicate clearly, while adhering to the ethical requirement to present data-derived information truthfully and without bias. The examples covered during the module have been generated using the R language. However, students can use other languages and visualisation applications in their assignment work.
(Language of instruction: English)
Learning Outcomes
- Describe the basic design principles underlying human perception, color theory and narrative
- Analyse the effectiveness of different visual elements in communicating analytical information
- Select the best visualisation strategy to use for different exploratory and explanatory scenarios
- Execute different types of data visualisations for use in various exploratory and explanatory scenarios
- Carry out basic data preprocessing and wrangling necessary to produce effective visualisations
- Discuss the ethical issues of representing data and information truthfully when creating a visualisation
- Critically evaluate data visualisations produced by other people
Assessments
- Written Assessment (65%)
- Continuous Assessment (35%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
Reading List
- "R Graphics Cookbook" by Winston Chang
Publisher: O'Reilly - "ggplot2" by Hadley Wickham
ISBN: 9783319242750.
Publisher: Springer - "Information Visualization" by Colin Ware
ISBN: 9780123814647.
Publisher: Elsevier - "Now You See it" by Stephen Few
ISBN: 9780970601988.
Publisher: Analytical Press - "The Visual Display of Quantitative Information" by Edward R. Tufte
ISBN: 9781930824133.
Publisher: Graphics Press
Note: Module offerings and details may be subject to change.
RequiredCT5134: Agents, Multi-Agent Systems and Reinforcement Learning
CT5134: Agents, Multi-Agent Systems and Reinforcement Learning
Semester 2 | Credits: 5
The topic of Agents and Multi-Agent Systems, examines environments that involve autonomous decision making software actors to interact with their surroundings with the aim of achieving some individual or overall goal. A typical agent environment could be a trading environment where an agent attempts to optimise energy usage, or the profitability of a transaction. More recently, significant global attention has focused on the vision of autonomous vehicles, which also follows the core principle of an agent attempting to achieve a set of defined goals.
This module begins by examining the underpinnings of what is an Agent, and how we can better understand the principles of an agent and its autonomy. Multi-Agent Systems are then explored, as a means of understanding how many agents can interact with each other in a complex environment. Agents are commonly modelled using Game Theory, and in this module a range of Game Theoretic Models will be studied.
The module will also examine Adaptive Learning Agents through the use of Reinforcement Learning, which focuses on training learners to choose actions which yield the maximum reward in the absence of prior knowledge. The module takes a hands-on, practical approach to reinforcement learning theory, beginning with Markov Decision Processes, detailing practical learning examples and how to formulate a reinforcement learning task.
(Language of instruction: English)
Learning Outcomes
- Explain and discuss the principles underlying Agents.
- Explain the role of game theory and games in agent design.
- Apply the principle of agents to a range of simulation problems.
- Formulate a decision making problem as a Markov decision process (MDP)
- Apply reinforcement learning algorithms to learn policies for MDPs
- Conduct experiments to determine appropriate hyperparameters for a reinforcement learning algorithm
Assessments
- Written Assessment (70%)
- Continuous Assessment (30%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
The above information outlines module CT5134: "Agents, Multi-Agent Systems and Reinforcement Learning " and is valid from 2024 onwards.Note: Module offerings and details may be subject to change.
RequiredCT5133: Deep Learning
CT5133: Deep Learning
Semester 2 | Credits: 5
This is an advanced module in machine learning, focusing on neural networks (NNs), deep NNs, and connectionist computing. Students learn about the basic principles and building blocks of deep learning, and how to implement a deep neural network ‘from scratch’. They also learn about software libraries and tools, and gain experience of applying deep learning in a range of practical applications. The module includes substantial practical programming assignments.
This module is intended for students who have completed a first course in machine learning, and already have a good grounding in supervised learning topics including: classification and regression; evaluation of classifiers; overfitting and underfitting; basic algorithms such as k-nearest neighbours, decision tree learning, logistic regression, and gradient descent.
(Language of instruction: English)
Learning Outcomes
- Explain key Machine Learning concepts that relate to Deep Learning
- Explain the operation of feed-forward neural networks and the back-propagation algorithm
- Describe, implement and apply key features of deep neural networks
- Implement NNs for supervised machine learning tasks, from first principles and (separately) using modern libraries and frameworks
- Choose, explain and implement: (a) recurrent and other NN architectures for sequential data; (b) self-supervised NN architectures for unlabelled data; (c) supervised NN architectures for representation learning
- Discuss ethical issues, limitations, and emerging trends in deep learning.
Assessments
- Written Assessment (60%)
- Continuous Assessment (40%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
- MICHAEL MADDEN 🖂
- DEIRDRE KING 🖂
- GERALDINE HEALY 🖂
- JAMES MCDERMOTT 🖂
- Bharathi Raja Asoka Chakravarthi 🖂
Note: Module offerings and details may be subject to change.
OptionalCT5105: Tools and Techniques for Large Scale Data Analytics
CT5105: Tools and Techniques for Large Scale Data Analytics
Semester 1 | Credits: 5
Large-scale data analytics is concerned with the processing and analysis of large quantities of data, typically from distributed sources (such as data streams on the internet). This module introduces students to state-of-the-art approaches to large-scale data analytics. Students learn about foundational concepts, software tools and advanced programming techniques for the scalable storage, processing and analysis of high- volume and high-velocity data, and how to apply them to practical problems.
** This module uses Java as programming language. Knowledge of Java is a prerequisite for participation in this module. **
Planned topics include: Definition of large-scale computational data analytics; Overview of approaches to the processing and analysis of high volume and high velocity data from distributed sources; Applications of large-scale data analytics; Foundations of cluster computing and parallel data processing; Introduction of selected relevant frameworks (e.g., Apache Hadoop and Spark). MapReduce; Advanced programming concepts for large-scale data analytics; Concepts and tools for large-scale data storage; Stream data analytics; Event Processing; Techniques and open-source tools for large-scale analytics; Computational statistics and machine learning with large-scale data processing frameworks such as Spark. Columnar data storage.
(Language of instruction: English)
Learning Outcomes
- Be able to define large-scale data analytics and understand its characteristics
- Be able to explain and apply concepts and tools for distributed and parallel processing of large-scale data
- Know how to explain and apply concepts and tools for highly scalable storage, querying, filtering, sorting and synthesizing of data
- Know how to describe and apply selected statistical and machine learning techniques and tools for the analysis of large-scale data
- Know how to explain and apply approaches to stream data analytics and event processing
Assessments
- Written Assessment (70%)
- Continuous Assessment (30%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
Reading List
- "Learning Spark: Lightning-Fast Big Data Analytics." by Holden Karau, Andy Konwinski, Patrick Wendell, Matei Zaharia
Publisher: O'Reilly - "Hadoop: The Definitive Guide" by Tom White
ISBN: 9781449311520.
Publisher: "O'Reilly Media, Inc." - "Large-Scale Data Analytics" by Aris Gkoulalas-Divanis,Abderrahim Labbi
ISBN: 1461492424.
Publisher: Springer Science & Business Media
Note: Module offerings and details may be subject to change.
OptionalCT561: Systems Modelling and Simulation
CT561: Systems Modelling and Simulation
Semester 1 | Credits: 5
Simulation is a quantitative method used to support decision making and predicting system behaviour over time. This course focuses the system dynamics approach. The course covers the fundamentals of simulation, and describes how to design and build mathematical models. Case studies used include: software project management, public health policy planning, and capacity planning.
(Language of instruction: English)
Learning Outcomes
- Define the aim of Simulation and its role in the decision making process for complex systems
- Distinguish between the two feedback types: positive and negative
- Demonstrate how to apply the system dynamics approach to areas including public health, software engineering management and capacity planning.
- Explain and apply numerical integration methods to solve simulation problems.
- Given a simulation problem, formulate a model, test the structure and equations, and perform detailed sensitivity analysis on the impact of a range of policy options
- Build, test and evaluate models using Vensim.
- Appreciate the differences between continuous and agent-based simulation
Assessments
- Written Assessment (70%)
- Continuous Assessment (30%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
The above information outlines module CT561: "Systems Modelling and Simulation" and is valid from 2024 onwards.Note: Module offerings and details may be subject to change.
OptionalCT5141: Optimisation
CT5141: Optimisation
Semester 1 | Credits: 5
This module covers optimisation -- "the science of better". Optimisation is used in a huge variety of applications, including: finding time-saving transport routes; scheduling exams without conflicts; reducing weight and cost in engineering design; designing portfolios of financial investments; finding numerical data models with low expected error; and many more. In this module we will aim to understanding a broad range of applications and a unifying view of the field, including four main types of methods: (1) exact methods for constrained optimisation (2) constructive heuristics (3) gradient descent (briefly) (4) metaheuristics. We will use labs for practical implementations, writing our own optimisation programs from scratch in Python and also using state-of-the-art libraries.
(Language of instruction: English)
Learning Outcomes
- Compare and contrast different algorithms and algorithm types (metaheuristics, constrained optimisation, gradient descent, constructive heuristics) and state their advantages and disadvantages for specific problems
- State common real-world applications of optimisation algorithms
- Implement a variety of metaheuristic algorithms, linear programming / constrained optimisation algorithms, and constructive heuristics, either from scratch or using library code, and interpret outcomes in a business or science application context
- Design novel algorithms or algorithm varieties to suit specific problems
Assessments
- Written Assessment (70%)
- Continuous Assessment (30%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
Reading List
- "Essentials of Metaheuristics" by Sean Luke
- "Programming Collective Intelligence" by Toby Segaran
Note: Module offerings and details may be subject to change.
OptionalCT5121: Advanced Topics in Natural Language Processing
CT5121: Advanced Topics in Natural Language Processing
Semester 2 | Credits: 5
Advanced topics in natural language processing, including deep learning for NLP, machine translation and language resources. This module covers topics in the following areas:
* Use of neural networks, deep learning and large language models for solving NLP tasks
* Advanced NLP techniques including textual similarity, event extraction and question answering.
* Multilingual and mulitmodal NLP techniques including machine translation
* Applications of NLP in digital humanities, legal NLP, language learning or other similar areas
(Language of instruction: English)
Learning Outcomes
- Use deep learning, neural networks and large language models for NLP
- Synthesize practical knowledge of NLP to complex tasks in data analytics.
- Apply multilingual NLP and build simple machine translation systems.
- Create complex solutions for real world problems using NLP technologies
- Solve novel NLP challenges using deep learning technologies
Assessments
- Written Assessment (50%)
- Continuous Assessment (50%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
The above information outlines module CT5121: "Advanced Topics in Natural Language Processing" and is valid from 2024 onwards.Note: Module offerings and details may be subject to change.
OptionalEE551: Embedded Image Processing
EE551: Embedded Image Processing
Semester 1 | Credits: 5
Content: This module covers the concepts and technology that are central to embedded image processing. The course material is supported by practical examples and laboratories/assignments using Python software.
Pre-requisites: (1) Knowledge of signal and system analysis including Fourier analysis and filtering. Knowledge of matrix algebra. (2) Knowledge of Python would be an advantage.
(Language of instruction: English)
Learning Outcomes
- Describe a digital image in terms of the image parameters, sensor characteristics, and colour space.
- Perform low-level operations (histogram processing, basic pixel-level processing) for image processing functions
- Design and apply appropriate spatial domain filters for image processing tasks, e.g. noise removal.
- Describe and apply frequency domain filtering techniques to digital images.
- Apply shape analysis and segmentation algorithms to digital images.
- Develop and apply feature detection algorithms to digital images.
- Develop a complex object detection system for digital images.
Assessments
- Continuous Assessment (100%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
Reading List
- "Digital Image Processing" by Rafael Gonzalez and Richard Woods
Publisher: Pearson - "Computer Vision: Algorithms and Applications" by Richard Szeliski
Publisher: Springer
Note: Module offerings and details may be subject to change.
OptionalCT5113: Web and Network Science
CT5113: Web and Network Science
Semester 2 | Credits: 5
Web and Network Science is concerned with techniques and technologies for analysing, modelling and learning from data that is represented as a graph. The module takes a practical approach to the analysis and modelling of large network data sets from multiple domains. Students will learn the fundamentals of graph theory and how to apply graph analysis, modelling and evaluation techniques to real data sets for applications such as recommendation, authority ranking, link-prediction and community detection. The practical work in this module is done using the R programming language - and learners are expected to be able to programme in R.
(Language of instruction: English)
Learning Outcomes
- Explain and discuss the theoretical principles behind graph analysis and modelling
- pre-process, load and analyse data in a variety of network formats and standards
- measure the fundamental properties of a graph
- apply fundamentals of comparative network modelling to real data sets
- apply the principles and techniques of graph partitioning, community-finding and modularity analysis to real network data
- apply the core techniques in social network analysis to analyse a real social graph
- apply graph analytical techniques to applications such as recommender systems, user role analysis and link prediction
- visualise network data, where appropriate
Assessments
- Written Assessment (50%)
- Continuous Assessment (50%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
Reading List
- "Networks" by Mark Newman
Publisher: Oxford - "Statistical Analysis of Network Data with R" by Eric D. D. Kolaczyk ; Gábor Csárdi
ISBN: 978-149390982.
Publisher: Springer
Note: Module offerings and details may be subject to change.
OptionalCT5187: Knowledge Representation
CT5187: Knowledge Representation
Semester 2 | Credits: 5
This module introduces students to Knowledge Representation (KR) and reasoning using formal logic.
Planned topics include: Foundations of knowledge representation. Propositional and first-order logic (FOL). Foundations of reasoning. Logic programming. Satisfiability Solving (SAT) and Answer Set Programming (ASP). Probabilistic logics and uncertainty reasoning. Basics of machine learning in the context of KR.
(Language of instruction: English)
Learning Outcomes
- Explain the fundamental principles of knowledge representation and reasoning
- Describe and use syntax and semantics of important formal logics
- Explain and be able to use fundamental types of reasoning and logic frameworks
- Model application domains using logic languages and relational knowledge representation formats
- Explain and apply selected Machine Learning models in the context of knowledge representation and reasoning
Assessments
- Written Assessment (70%)
- Continuous Assessment (30%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
Reading List
- "Knowledge Representation and Reasoning" by Ronald J. Brachman, Hector J. Levesque
Publisher: Elsevier/Morgan Kaufmann
Note: Module offerings and details may be subject to change.
OptionalCT5166: Knowledge Graphs
CT5166: Knowledge Graphs
Semester 2 | Credits: 5
Knowledge graphs are a fundamental part of many enterprise systems and this module will teach the fundamentals for working with knowledge graphs. This will focus on how knowledge graphs can be applied in enterprise and students will learn about the data models for knowledge graphs (including semantic web standards such as RDF), reasoning over knowledge graphs, linked open data, knowledge graphs in enterprise, knowledge graph extraction and knowledge graph linking. As such this module will teach the general principles of knowledge graphs alongside the technical tools used for these. This module will also build on techniques from artificial intelligence and natural language processing to show how automatic tools can create and manipulate knowledge graphs.
(Language of instruction: English)
Learning Outcomes
- Explain how knowledge graphs can be used in enterprise
- Model real-world knowledge as a graph and apply reasoning over this graph
- Use standards such as RDF to represent a knowledge graph
- Explain how knowledge graphs can be extracted from text and other sources
- Be able to integrate knowledge graphs into real-world enterprise applications
Assessments
- Written Assessment (60%)
- Continuous Assessment (40%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
Reading List
- "Exploiting Linked Data and Knowledge Graphs for Large Organisations" by Jeff Z. Pan,Guido Vetere,Jose Manuel Gomez- Perez,Honghan Wu
ISBN: 9783319456522.
Publisher: Springer - "Knowledge Graphs" by Dieter Fensel,Umutcan Şimşek,Kevin Angele,Elwin Huaman,Elias Kärle,Oleksandra Panasiuk,Ioan Toma,Jürgen Umbrich,Alexander Wahler
ISBN: 9783030374389.
Publisher: Springer
Note: Module offerings and details may be subject to change.
OptionalCT5100: Data Visualisation
CT5100: Data Visualisation
Semester 2 | Credits: 5
Visualisation is a fundamental technique in presenting the properties of data and the results and evaluation of data analytical processes. For a data visualisation to be successful the analyst needs to have considered the properties of the data, the information to be communicated, the mode of visualisation delivery and the expectations of the audience. This module takes a practical approach to introducing learners to the strengths and weaknesses of human perception, and the use of best practices to represent complex and large data stories using visual primitives. The module demonstrates the role of visualisation in exploratory data analysis and its fundamental role in explaining data analytical outcomes. The module emphasises the need to communicate clearly, while adhering to the ethical requirement to present data-derived information truthfully and without bias. The examples covered during the module have been generated using the R language. However, students can use other languages and visualisation applications in their assignment work.
(Language of instruction: English)
Learning Outcomes
- Describe the basic design principles underlying human perception, color theory and narrative
- Analyse the effectiveness of different visual elements in communicating analytical information
- Select the best visualisation strategy to use for different exploratory and explanatory scenarios
- Execute different types of data visualisations for use in various exploratory and explanatory scenarios
- Carry out basic data preprocessing and wrangling necessary to produce effective visualisations
- Discuss the ethical issues of representing data and information truthfully when creating a visualisation
- Critically evaluate data visualisations produced by other people
Assessments
- Written Assessment (65%)
- Continuous Assessment (35%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
Reading List
- "R Graphics Cookbook" by Winston Chang
Publisher: O'Reilly - "ggplot2" by Hadley Wickham
ISBN: 9783319242750.
Publisher: Springer - "Information Visualization" by Colin Ware
ISBN: 9780123814647.
Publisher: Elsevier - "Now You See it" by Stephen Few
ISBN: 9780970601988.
Publisher: Analytical Press - "The Visual Display of Quantitative Information" by Edward R. Tufte
ISBN: 9781930824133.
Publisher: Graphics Press
Note: Module offerings and details may be subject to change.
The MSc is a 1 year, fulltime, 90-ECTS course with three main elements: core modules (45 ECTS), optional (elective) modules (15 ECTS), and a substantial capstone thesis project (30 ECTS).
Opportunities
AI skills will be required in every industry and AI is projected—according to recent World Economic Forum research—to create globally around 97 million new jobs. The World Economic Forum estimates that by 2025 machines are expected to perform more current work tasks than humans compared to 71% being performed by humans today, and a PwC-report concludes that artificial intelligence, robotics and smart automation technology could contribute up to $15.7 trillion to the global GDP by 2030.
Within the AI space, there is a diversity of jobs requiring various levels of expertise:
- More foundational jobs include data architects, software engineers and machine and deep learning engineers.
- Advanced roles include specialist research engineers, including those that specialise in computer vision, language and speech, and AI architects.
How will I learn?
The MSc in AI combines innovative teaching methods with practical, hands-on learning to ensure a comprehensive educational experience. You will learn through a mix of interactive lectures, seminars and workshops led by expert faculty. Real-world case studies, data-driven projects and coding exercises will enable you to apply theoretical knowledge to practical problems.
Group projects and collaborative activities will enhance your teamwork and communication skills, while individual assignments and the final dissertation will help you develop independence and critical thinking.
Throughout the programme, you will have access to cutting-edge resources, including industry-standard software and real-world datasets, to support your learning and professional growth
How Will I Be Assessed?
Throughout the programme, your progress is assessed through various coursework and exams, including reports, essays, presentations, and computer assignments.
- Continuous Assessment- Regular coursework, including essays, presentations, in-class tests, and language exercises. Students receive regular (weekly) feedback on their progress.
- Examinations- Written exams take place before Christmas and in May. Written and oral exams evaluate proficiency in grammar, vocabulary, comprehension, and communication.
- Project Work- Research and subtitling projects and translation assignments allow students to apply their skills in real-world contexts.
Course queries:
E: MScCS-AI@univeristyofgalway.ie
T: +353 91 493 836
Programme Director(s):
Dr Bharathi Chakravarthi,
Lecturer in Computer Science
School of Computer Science
College of Science & Engineering
University of Galway recognises that knowledge and skills can be acquired from a range of learning experiences. This is in line with the National Framework of Qualifications (NFQ) goals which aim to recognise all learning achievements by supporting the development of alternative pathways to qualifications (or awards) and by facilitating the recognition of prior learning (RPL).
This programme is designed to provide early and mid-career accountants with the skills and knowledge needed to engage with big data in a variety of roles in practice and industry.
Candidates who have completed all of the professional accounting examinations and have been admitted as full members by a recognised professional accountancy body including the following: ACCA, CIPFA, CIMA, ICAEW, ICAI, ICAS or other IFAC member body assessed as equivalent by the academic programme director, are eligible for consideration.
Graduates of the MSc in AI will be able to:
- Strong Programming & Mathematical Foundation: Proficiency in programming languages and a solid grasp of linear algebra, calculus, probability, and statistics are fundamental for understanding and implementing AI algorithms.
- Data Handling and Analytical Skills: The ability to clean, transform, analyse, and visualise data is crucial, along with a keen problem-solving and critical thinking mindset to dissect complex AI challenges.
- Research and Independent Learning: Given the rapidly evolving nature of AI, skills in information retrieval, academic research methods, and a strong capacity for continuous self-directed learning are vital for success in the program's research components and beyond.
- Effective Communication: Clearly articulating complex technical concepts, research findings, and project outcomes through both written reports and verbal presentations is essential for academic collaboration and future career progression.
- Teamwork and Ethical Awareness: The ability to collaborate effectively on projects and a strong understanding of the ethical implications and societal impact of AI are increasingly important skills for responsible AI development. Apply enhanced critical thinking and analytical skills to their object of study
- Plan, manage, and execute a substantial independent study project
- Reflect deeply on a range of research perspectives, topics, and approaches related to the object of study
- Exhibit the ability to self-assess and self-direct
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Entry Requirements and Fees
This MSc is targeted at high-performing graduates of Level 8 computer science programmes, or Level 8 science/engineering programmes that offer sufficient training in computing.
The minimum academic requirement for entry to the programme is a First Class Honours (or equivalent) from a recognised university or third-level college. However, a good Second Class Honours (or equivalent) can be deemed sufficient on the recommendation of the Programme Director.
Academic entry requirements standardised per country are available here.
English Language Entry Requirements
Overall, entry to the MSc Artificial Intelligence requires a minimum IELTS score of 6.5 overall, 6.5 in Writing and no less than 6.0 in any other band. TOEFL: Overall 88, Listening 12–19, Speaking 18–19, Writing 24–26, Reading 13–18. PTE: Overall 61, Writing 61, all other bands no less than 50.
More information on English language test equivalency are available here.
Supporting Documents
You will be required to provide supporting documentation as part of your application. You can check here what supporting documents are required for this course.
You can apply online to the University of Galway application portal here.
Please review the entry requirements set out in the section above.
You will be required to upload supporting documentation to your application electronically. See the section above on entry requirements for further information on the supporting documentation required for this course.
Closing Dates
For this programme, there is no specific closing date for receipt of applications. Applications will be accepted on a rolling basis and course quotes will be reviewed continuously throughout the application cycle.
Notes
- You will need an active email account to use the website and you'll be guided through the system, step by step, until you complete the online form.
- Browse the FAQ's section for further guidance.
Fees for Academic Year 2025/2026
Course Type | Year | EU Tuition | Student Contribution | Non-EU Tuition | Levy | Total Fee | Total EU Fee | Total Non-EU Fee |
---|---|---|---|---|---|---|---|---|
Masters Full Time | 1 | €8,750 | €28,000 | €140 | €8,890 | €28,140 |
For 25/26 entrants, where the course duration is greater than 1 year, there is an inflationary increase approved of 3.4% per annum for continuing years fees.
Postgraduate students in receipt of a SUSI grant – please note an F4 grant is where SUSI will pay €4,000 towards your tuition (2025/26). You will be liable for the remainder of the total fee. A P1 grant is where SUSI will pay tuition up to a maximum of €6,270. SUSI will not cover the student levy of €140.
Note to non-EU students: learn about the 24-month Stayback Visa here.
The School of Computer Science Advanced MSc Scholarship (Artificial Intelligence, Data Analytics)
University of Galway, School of Computer Science is offering one scholarship for the MSc in Computer Science (Artificial Intelligence, Data Analytics) commencing the next academic year; this scholarship, up to the full value of the EU tuition fee is awarded to the highest ranked graduate of the BSc Computer Science & IT undergraduate programme (GY350) among those who apply for the scholarship. This is open to all current GY350 final year students due to graduate in the current academic year, and all previous graduates of the programme.
Please note that to be considered for this merit-based scholarship, applicants must first apply and be accepted to their chosen programme. They will subsequently be required to complete a separate scholarship application form.
Other scholarships available
Find out about our Postgraduate Scholarships here.
You may also be interested in one of our other School of Computer Science postgraduate programmes.
Applications are made online via the University of Galway Postgraduate Applications System.
Application requirements:
- A personal statement
- A CV
- University Degree Transcripts
- Two references
- IELTS/TOEFL certificate—only if English is not your mother tongue
What is not required (please do not submit these)
- secondary school certificates
- training certificates
- membership certificates
Application Process
Students applying for full time postgraduate programmes from outside of the European Union (EU), You can apply online to the University of Galway application portal here.
Our application portal opens on the 1st October each year for each the following September.
Further Information
Please visit the postgraduate admissions webpage for further information on closing dates, documentation requirements, application fees and the application process.
Why University of Galway?
World renowned research led university nestled in the vibrant heart of Galway city on Ireland's scenic West Coast.
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Course Introduction
AI: The Intersection of Computing and Creativity
The full-time MSc in Artificial Intelligence is taught by renowned, interdisciplinary University of Galway experts in the field. It covers over two semesters many complementary areas of Artificial Intelligence, including Deep Learning, Natural Language Processing, Optimisation, and Reinforcement Learning.
MSc Artificial Intelligence Brochure