MSc (Computer Science—Artificial Intelligence [Online])

Program Overview

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Course Overview

Artificial Intelligence (AI) has been at the forefront of computer science research for over 50 years. In recent years a confluence of breakthroughs in hardware capability and insights into algorithm design have made the early promise of intelligent machines a reality. AI is one of the fastest growing areas of ICT industry and research. It has the potential to positively transform every aspect of all our lives, from smart cities and autonomous vehicles, through to improved   healthcare services and low-carbon economies.

This is a distinctive programme taught by an internationally renowned, interdisciplinary team of University of Galway experts in the field, many of whom are researchers at the

Insight Centre for Data Analytics.

Students also have the opportunity to choose from several optional online modules on offer from our partner in this programme

Dublin City University (DCU).

The programme is taught over two years and is delivered completely online using state-of-the-art technologies and techniques to support the virtual classroom. Students are expected to attend classes on campus at most one day per semester.

This is an intensive and technically rigorous programme. The estimated workload is 20 hours per week during the teaching semesters (September–December and January– April). Each semester is 12 weeks plus a study week and exam period.

This course is part-funded by Technology Ireland ICT Skillnet under the Training Networks Programme of Skillnet Ireland and by member companies. Skillnet Ireland is funded from the National Training Fund through the Department of Education and Skills. For further information see www.ictskillnet.ie

Scholarships Available

Find out about our Postgraduate Scholarships here.

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You may also be interested in one of our other

Co

m

puter Science and Software Engineering

postgraduate programmes.

Program Outline

Course Outline

The MSc in Computer Science—Artificial Intelligence (online) is a two-year 90-ECTS course taught online comprising:

12 taught modules in core AI topics (60 ECTS)

A substantial capstone project (30 ECTS).

The taught modules include:

Machine Learning;

Deep Learning;

Natural Language Processing;

Programming and Tools for Artificial Intelligence;

Tools and Techniques for Large Scale Data Analytics;

Research Skills in Artificial Intelligence

Reinforcement Learning and Multi-Agent Systems;

Data Visualisation;

Knowledge Representation & Statistical Relational Learning;

Information Retrieval;

Ethics in Artificial Intelligence;

Students at University of Galway will also have the opportunity to choose from the following optional online modules on offer from Dublin City University.

Machine Translation;

Mechanics of Search.

From Year 1, Semester 2 onwards, students work on industry-focused projects and submit them in August Year 2. Projects may have a research or applied focus.

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Curriculum Information

Curriculum information relates to the current academic year (in most cases).

Course and module offerings and details may be subject to change.

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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.

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Year 1 (90 Credits)

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Optional

CT5148:

Programming and Tools for Artificial Intelligence - Online

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CT5148: Programming and Tools for Artificial Intelligence - Online

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Semester 1 | Credits: 5

Overview This module will provide students with the programming and AI toolkit skills they will need for the modules in the MSc in Artificial Intelligence. The focus will on the Python programming language and libraries, but there will also be two weeks of R programming, focusing on using R for statistical analysis. Topics Introductory Python: writing and executing Python code through an IDE, command line, or notebook; arithmetic; syntax; comments and doc-strings; variables; functions; loops and conditionals; lists, tuples, dictionaries; classes; input/output; testing. Python data libraries: Numpy, Pandas, Matplotlib, and friends. Introductory R: R for statistics. Python libraries for AI: Scikit-learn , PyTorch, Keras or another modern neural network library. Version control and cloud execution.

(Language of instruction: English)

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Learning Outcomes

1. Read and write simple Python programs, e.g. for data munging, with a high degree of comfort.
2. Use R for simple statistics and data exploration.
3. Use Python libraries for manipulation, input/output, visualisation of numerical data using Numpy array types.
4. Use the Scikit-learn API for regression tasks.
5. Construct, train and use neural networks using a modern Python library.
6. Plan/design a program using any of the above facilities; test it; document it; execute it locally or in the cloud, and using GPU where appropriate.

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Assessments

This module's usual assessment procedures, outlined below, may be affected by COVID-19 countermeasures. Current students should check Blackboard for up-to-date assessment information.

Written Assessment (50%)

Continuous Assessment (50%)

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Module Director

JAMES MCDERMOTT:

Research Profile

| Email

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Lecturers / Tutors

JAMES MCDERMOTT:  Research Profile

LORNA MOYLAN:  Research Profile

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Reading List

1. "A Whirlwind Tour of Python," by Jake Vanderplas
2. "Think Python 2nd edition" by Allen B. Downey
3. "Python for Data Analysis" by Wes McKinney
4. "Programming Collective Intelligence: Building Smart Web 2.0 Applications," by Toby Segaran

The above information outlines module CT5148: "Programming and Tools for Artificial Intelligence - Online" and is valid from 2022 onwards.

Note: Module offerings and details may be subject to change.

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Optional

CT5170:

Principles of Machine Learning - Online

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CT5170: Principles of Machine Learning - Online

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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 task 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 applying 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 8. Reinforcement Learning.

(Language of instruction: English)

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Learning Outcomes

1. Define Machine Learning and explain what major categories of learning task entail
2. Demonstrate how to apply the machine learning and data mining process to practical problems
3. Explain and apply algorithms including decision tree learning, instance-based learning, probabilistic learning, linear regression, logistic regression, Q-learning, and others
4. Given a dataset and task to be addressed, select, apply and evaluate appropriate algorithms, and interpret the results
5. Discuss ethical issues and emerging trends in machine learning.

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Assessments

This module's usual assessment procedures, outlined below, may be affected by COVID-19 countermeasures. Current students should check Blackboard for up-to-date assessment information.

Written Assessment (60%)

Continuous Assessment (40%)

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Module Director

JAMES MCDERMOTT:

Research Profile

| Email

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Lecturers / Tutors

MICHAEL MADDEN:  Research Profile

Ihsan Ullah:  Research Profile

The above information outlines module CT5170: "Principles of Machine Learning - Online" and is valid from 2022 onwards.

Note: Module offerings and details may be subject to change.

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Optional

ST5001:

Statistics for Artificial Intelligence

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ST5001: Statistics for Artificial Intelligence

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Semester 1 | Credits: 5

This module provides students with an introduction to Statistics and the use of statistical modelling in the domain of Artificial Intelligence (AI). The course will start with a discussion of the overlap and differences between Data Science, Statistics, Machine Learning and Statistical Learning. The critical role of probability as a data generating mechanism will be explored with particular emphasis on the Binomial, Poisson, Exponential and Normal distributions. The key role of study design and the methods for parameter estimation and uncertainty using classical and computational approaches will be covered in detail. The remainder of the course will involve the use of statistical modelling in experimental and observational studies, small and large, in a wide variety of contexts by fitting and interpreting relevant statistical models in R.

(Language of instruction: English)

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Learning Outcomes

1. Demonstrate the use of probability as a data generating mechanism.
2. Present data in a visually compelling manner with an emphasis on best practice for communication.
3. Apply modern statistical modelling techniques to analyse complex study designs using R.
4. Compile a statistical report using the principles of reproducible research.

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Assessments

This module's usual assessment procedures, outlined below, may be affected by COVID-19 countermeasures. Current students should check Blackboard for up-to-date assessment information.

Written Assessment (70%)

Continuous Assessment (30%)

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Module Director

JOHN NEWELL:

Research Profile

| Email

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Lecturers / Tutors

NOELLE GANNON:  Research Profile

The above information outlines module ST5001: "Statistics for Artificial Intelligence" and is valid from 2021 onwards.

Note: Module offerings and details may be subject to change.

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Optional

CT5152:

Artificial Intelligence and Ethics - Online

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CT5152: Artificial Intelligence and Ethics - Online

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Semester 1 | Credits: 5

Overview Artificial 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 Philosophy of Technology, Value Sensitive Design, Responsible Research and Innovation (RRI), Privacy and consent, Contextual integrity, Transparency and explainable AI, Trust and Trustworthiness, Datafication, Algorithmic surveillance, Algorithmic Bias, Autonomous artificial agents and responsibility, and Human replacement Topics 1. Philosophy of Technology 2. Value Sensitive Design 3. Responsible Research and Innovation (RRI) 4. Privacy and consent 5. Contextual integrity 6. Transparency and explainable AI 7. Trust and Trustworthiness 8. Datafication 9. Algorithmic surveillance 10. Algorithmic Bias 11. Autonomous artificial agents and responsibility 12. Human replacement.

(Language of instruction: English)

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Learning Outcomes

1. Identify and summarise important ethical concerns related to the design, use and societal impact of Artificial Intelligence
2. Apply relevant theoretical models from the ethical, legal and social science literature to identified ethical concerns regarding AI.
3. 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 Artificial Intelligence.
4. Demonstrate the ability to communicate core insights from divergent perspectives on ethical concerns coherently and concisely.

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Assessments

This module's usual assessment procedures, outlined below, may be affected by COVID-19 countermeasures. Current students should check Blackboard for up-to-date assessment information.

Continuous Assessment (100%)

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Module Director

JAMES MCDERMOTT:

Research Profile

| Email

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Lecturers / Tutors

HEIKE SCHMIDT-FELZMANN:  Research Profile

JAMES MCDERMOTT:  Research Profile

LORNA MOYLAN:  Research Profile

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Reading List

1. "Towards a Code of Ethics for Artificial Intelligence" by Paula Boddington

   Publisher: Springer

2. "Privacy in Context: Technology, Policy, and the Integrity of Social Life," by Helen Nissenbaum

   Publisher: Stanford University Press

3. "Privacy as Trust: Information Privacy for an Information Age" by Ari Waldman

   Publisher: Cambridge University Press

4. "Privacy, Big Data, and the Public Good: Frameworks for Engagement" by Julia Lane, Victoria Stodden, Stefan Bender, Helen Nissenbaum (Editors)

   Publisher: Cambridge University Press

5. "Robot Ethics 2.0: From Autonomous Cars to Artificial Intelligence" by Patrick Lin, Keith Abney, Ryan Jenkins (Editors)

   Publisher: Oxford University Press

6. "Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor" by Virginia Eubanks

   Publisher: St Martin's Press

The above information outlines module CT5152: "Artificial Intelligence and Ethics - Online" and is valid from 2022 onwards.

Note: Module offerings and details may be subject to change.

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Optional

CT5144:

Research Skills in Artificial Intelligence

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CT5144: Research Skills in Artificial Intelligence

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Semester 2 | Credits: 5

Exploring artificial intelligence through scientific writing and presentation skills. Topics include: Exploring Science & Technology; Scientific Method; Technology Waves; Information Revolution; Innovation and Creativity; Academic Writing; Referencing and Research Tools; Presentation Skills.

(Language of instruction: English)

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Learning Outcomes

1. Explore relationships between science, technology and innovation
2. Develop a scientific approach to problem solving in Artificial Intelligence
3. Develop skills in writing and reporting in the scientific style
4. Develop experience in research presentations
5. Publish literature review for a research topic in Artificial Intelligence

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Assessments

This module's usual assessment procedures, outlined below, may be affected by COVID-19 countermeasures. Current students should check Blackboard for up-to-date assessment information.

Continuous Assessment (100%)

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Module Director

JAMES MCDERMOTT:

Research Profile

| Email

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Lecturers / Tutors

DAVID O'SULLIVAN:  Research Profile

The above information outlines module CT5144: "Research Skills in Artificial Intelligence" and is valid from 2022 onwards.

Note: Module offerings and details may be subject to change.

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Optional

CT5146:

Introduction to Natural Language Processing - Online

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CT5146: Introduction to Natural Language Processing - Online

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Semester 1 | Credits: 5

Introduction to natural language processing, including foundations in linguistics, statistical analysis and applications.

(Language of instruction: English)

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Learning Outcomes

1. Understand the various levels of linguistic structure relevant to NLP.
2. Use and understand standard algorithms for basic NLP analysis
3. Gain practical knowledge of and experience in the use of NLP toolkits
4. Understand the theoretical principles behind core NLP applications.
5. Apply NLP algorithms, toolkits and applications to Data Analytics tasks.

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Assessments

This module's usual assessment procedures, outlined below, may be affected by COVID-19 countermeasures. Current students should check Blackboard for up-to-date assessment information.

Written Assessment (50%)

Continuous Assessment (50%)

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Module Director

JAMES MCDERMOTT:

Research Profile

| Email

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Lecturers / Tutors

PETER PAUL BUITELAAR:  Research Profile

JAMES MCDERMOTT:  Research Profile

LORNA MOYLAN:  Research Profile

The above information outlines module CT5146: "Introduction to Natural Language Processing - Online" and is valid from 2022 onwards.

Note: Module offerings and details may be subject to change.

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Optional

CT5153:

Information Retrieval - Online

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CT5153: Information Retrieval - Online

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Semester 1 | Credits: 5

The course introduces some of the main theories and techniques in the domain of information retrieval.

(Language of instruction: English)

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Learning Outcomes

1. Explain the main models used information retrieval.
2. Explain the factors involved in designing and analysing weighting schemes
3. Be able to chose suitable data structures and algorithms for builing IR systems
4. Be able to explain the main ideas and approaches used web search, collaborative filtering, multimedia IR

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Assessments

This module's usual assessment procedures, outlined below, may be affected by COVID-19 countermeasures. Current students should check Blackboard for up-to-date assessment information.

Written Assessment (70%)

Continuous Assessment (30%)

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Module Director

JAMES MCDERMOTT:

Research Profile

| Email

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Lecturers / Tutors

COLM O'RIORDAN:  Research Profile

JAMES MCDERMOTT:  Research Profile

LORNA MOYLAN:  Research Profile

The above information outlines module CT5153: "Information Retrieval - Online" and is valid from 2022 onwards.

Note: Module offerings and details may be subject to change.

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Optional

CT5131:

Capstone Project and Thesis in Artificial Intelligence - Online

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CT5131: Capstone Project and Thesis in Artificial Intelligence - Online

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15 months long | Credits: 30

Capstone Project and Minor Thesis in Artificial Intelligence (30 ECTS)

(Language of instruction: English)

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Learning Outcomes

1. apply a variety of AI techniques to solve a real world problem
2. diagnose a problem and design an AI based solution
3. conduct and report on exploratory analysis of the problem domain
4. produce an in-depth report (thesis) describing the problem, the diagnosis and approaches to solving it
5. demonstrate that they can research, apply and evaluate state-of-the-art techniques in artificial intelligence

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Assessments

This module's usual assessment procedures, outlined below, may be affected by COVID-19 countermeasures. Current students should check Blackboard for up-to-date assessment information.

Research (100%)

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Module Director

JAMES MCDERMOTT:

Research Profile

| Email

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Lecturers / Tutors

JAMES MCDERMOTT:  Research Profile

LORNA MOYLAN:  Research Profile

The above information outlines module CT5131: "Capstone Project and Thesis in Artificial Intelligence - Online" and is valid from 2022 onwards.

Note: Module offerings and details may be subject to change.

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Optional

CT5136:

Data Visualisation - Online

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CT5136: Data Visualisation - Online

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Semester 2 | Credits: 5

Data Visualisation is concerned with techniques and technologies for the visual representation of data and the results and evaluation of data analytic processes. 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 analytics outcomes. The practical work in this module is done using the R programming language - and learners are expected to have completed an introductory module in R. Topics covered • The properties of human visual perception • Visualisation libraries in R • Data wrangling for visualisation • Data visualisation in exploratory data analysis • Visualising variation in category variables • Visualising distributions • Visualising data over time; representing trends (Time Series) • Visualising relationships and correlations • Visualising multivariate data • Visualising textual data • Interactive visualisation techniques

(Language of instruction: English)

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Learning Outcomes

1. Analyse the effectiveness of different visual elements in communicating analytical information
2. Visually explore, analyse and explain new data
3. Make informed choices on the best visualisation strategies to use for different exploratory and explanatory scenarios
4. Build a variety of data visualisations using the R base libraries, ggplot2 and other processing libraries from the tidyverse set of R packages
5. Build custom visualisations that represent specific data-driven narratives
6. Outline one research area on data visualisation based on your reading of at least two research papers

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Assessments

This module's usual assessment procedures, outlined below, may be affected by COVID-19 countermeasures. Current students should check Blackboard for up-to-date assessment information.

Continuous Assessment (100%)

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Module Director

CONOR HAYES:

Research Profile

| Email

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Lecturers / Tutors

CONOR HAYES:  Research Profile

LORNA MOYLAN:  Research Profile

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Reading List

1. "ggplot2" by Hadley Wickham

   ISBN: 9783319242750.

   Publisher: Springer

2. "Information Visualization" by Colin Ware

   ISBN: 9780123814647.

   Publisher: Elsevier

3. "Now You See it" by Stephen Few

   ISBN: 9780970601988.

4. "The Visual Display of Quantitative Information PAPERBACK" by Edward R. Tufte

   ISBN: 9781930824133.

5. "R Graphics Cookbook, 2nd Edition" by Winston Chang

   ISBN: 9781491978597.

The above information outlines module CT5136: "Data Visualisation - Online" and is valid from 2022 onwards.

Note: Module offerings and details may be subject to change.

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Optional

CT5150:

Tools and Techniques for Large Scale Data Analytics - Online

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CT5150: Tools and Techniques for Large Scale Data Analytics - Online

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Semester 2 | 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 predictive analysis of high- volume and high-velocity data, and how to apply them to practical problems. <p><p> ** This module uses Java as programming language. Knowledge of Java is a prerequisite for participation in this module. ** <p><p> 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; The Hadoop and Spark ecosystems. MapReduce; Advanced programming concepts for large-scale data analytics; Concepts and tools for large-scale data storage; Stream data analytics. Complex Event Processing (CEP); Techniques and open-source tools for large-scale predictive analytics; Computational statistics and machine learning with large-scale data processing frameworks such as Spark; Privacy in the context of large-scale data analytics.

(Language of instruction: English)

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Learning Outcomes

1. Be able to define large-scale data analytics and understand its characteristics
2. Be able to explain and apply concepts and tools for distributed and parallel processing of large-scale data
3. Know how to explain and apply concepts and tools for highly scalable collection, querying, filtering, sorting and synthesizing of data
4. Know how to describe and apply selected statistical and machine learning techniques and tools for the analysis of large-scale data
5. Know how to explain and apply approaches to stream data analytics and complex event processing
6. Understand and be able to discuss privacy issues in connection with large-scale data analytics

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Assessments

This module's usual assessment procedures, outlined below, may be affected by COVID-19 countermeasures. Current students should check Blackboard for up-to-date assessment information.

Written Assessment (70%)

Continuous Assessment (30%)

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Module Director

MATTHIAS NICKLES:

Research Profile

| Email

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Lecturers / Tutors

MATTHIAS NICKLES:  Research Profile

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Reading List

1. "Learning Spark: Lightning-Fast Big Data Analytics." by Holden Karau, Andy Konwinski, Patrick Wendell, Matei Zaharia

   Publisher: O'Reilly

2. "Hadoop: The Definitive Guide" by Tom White

   ISBN: 9781449311520.

   Publisher: O'Reilly Media

3. "Large-Scale Data Analytics" by Gkoulalas-Divanis, Aris, Labbi, Abderrahim (Eds.)

   ISBN: 1461492424.

   Publisher: Springer

The above information outlines module CT5150: "Tools and Techniques for Large Scale Data Analytics - Online" and is valid from 2022 onwards.

Note: Module offerings and details may be subject to change.

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Optional

CT5145:

Deep Learning - Online

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CT5145: Deep Learning - Online

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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 learning 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)

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Learning Outcomes

1. Discuss and apply feature engineering and ensembles to improve the performance of classification and regression algorithms, and explain their relationship to connectionist computing
2. Explain the operation of feed-forward neural networks and the back-propagation algorithm
3. Describe, implement and apply key features of deep learning
4. Implement NNs for supervised machine learning tasks, from first principles and (separately) using modern libraries and frameworks
5. Diagnose common NN problems such as overfitting and underfitting and propose solutions
6. Choose, explain and implement convnet design for image processing tasks
7. Choose, explain and implement advanced architectures for specialised tasks
8. Discuss ethical issues, limitations, and emerging trends in deep learning.

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Assessments

This module's usual assessment procedures, outlined below, may be affected by COVID-19 countermeasures. Current students should check Blackboard for up-to-date assessment information.

Written Assessment (70%)

Continuous Assessment (30%)

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Module Director

JAMES MCDERMOTT:

Research Profile

| Email

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Lecturers / Tutors

MICHAEL MADDEN:  Research Profile

JAMES MCDERMOTT:  Research Profile

LORNA MOYLAN:  Research Profile

The above information outlines module CT5145: "Deep Learning - Online" and is valid from 2022 onwards.

Note: Module offerings and details may be subject to change.

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Optional

CT5130:

Agents, Multi-Agent Systems and Reinforcement Learning - Online

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CT5130: Agents, Multi-Agent Systems and Reinforcement Learning - Online

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Semester 2 | Credits: 5

Overview The topic of Agents and Multi-Agent Systems, examines environment 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 focussed 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 examine Adaptive Learning Agents through the use of Reinforcement Learning algorithms an area of Machine 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 in discrete environments and how to formulate a reinforcement learning task. It then extends this to continuous problem spaces, detailing Deep Reinforcement Learning with a practical implementation of a Deep Q Network using Keras.

(Language of instruction: English)

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Learning Outcomes

1. Explain and discuss the principles underlying Agents
2. Explain the role of game theory and games in agent design.
3. Apply the principle of agents to a range of simulation problems.
4. Understand the theory unpinning reinforcement learning
5. Apply reinforcement learning to a real-world problem
6. Apply advanced deep reinforcement learning approaches to a real-world problem.

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Assessments

This module's usual assessment procedures, outlined below, may be affected by COVID-19 countermeasures. Current students should check Blackboard for up-to-date assessment information.

Written Assessment (70%)

Continuous Assessment (30%)

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Module Director

JAMES MCDERMOTT:

Research Profile

| Email

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Lecturers / Tutors

ENDA HOWLEY:  Research Profile

JAMES MCDERMOTT:  Research Profile

PATRICK MANNION:  Research Profile

The above information outlines module CT5130: "Agents, Multi-Agent Systems and Reinforcement Learning - Online" and is valid from 2022 onwards.

Note: Module offerings and details may be subject to change.

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Optional

CT5188:

Knowledge Representation - Online

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CT5188: Knowledge Representation - Online

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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 (e.g., deductive, inductive, probabilistic). Logic programming. Satisfiability Solving and Answer Set Programming. Probabilistic logics and uncertainty reasoning. Basics of machine learning in the context of KR.

(Language of instruction: English)

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Learning Outcomes

1. Explain the fundamental principles of knowledge representation and reasoning
2. Correctly describe and deploy the syntax and semantics of important non-probabilistic and probabilistic logics
3. Explain and decide on the appropriate use of fundamental types of and approaches to reasoning
4. Model simple application domains using logic languages and relational knowledge representation formats
5. Explain and apply fundamental principles of Machine Learning in the context of KR

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Assessments

This module's usual assessment procedures, outlined below, may be affected by COVID-19 countermeasures. Current students should check Blackboard for up-to-date assessment information.

Written Assessment (70%)

Continuous Assessment (30%)

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Module Director

MATTHIAS NICKLES:

Research Profile

| Email

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Lecturers / Tutors

MATTHIAS NICKLES:  Research Profile

LORNA MOYLAN:  Research Profile

The above information outlines module CT5188: "Knowledge Representation - Online" and is valid from 2022 onwards.

Note: Module offerings and details may be subject to change.

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Optional

PH504:

High Performance Computing and Parallel Programming

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PH504: High Performance Computing and Parallel Programming

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Semester 2 | Credits: 5

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Assessments

This module's usual assessment procedures, outlined below, may be affected by COVID-19 countermeasures. Current students should check Blackboard for up-to-date assessment information.

Continuous Assessment (100%)

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Module Director

REBECCA NOLAN:

Research Profile

| Email

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Lecturers / Tutors

NICHOLAS DEVANEY:  Research Profile

SIMON WONG:  Research Profile

REBECCA NOLAN:  Research Profile

The above information outlines module PH504: "High Performance Computing and Parallel Programming" and is valid from 2021 onwards.

Note: Module offerings and details may be subject to change.

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Optional

DCU_CT558:

Further Topics in AI - Online - DCU

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DCU_CT558: Further Topics in AI - Online - DCU

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Semester 2 | Credits: 15

This module captures study carried out by students of the MSc in Computer Science - Artificial Intelligence (Online) as visiting students of DCU. They take two DCU taught online modules offered as part of the DCU Masters in Computing (Artificial Intelligence Online stream). Each of these is a 7.5 ECTS module in the DCU system, therefore by taking both, the student achieves the equivalent of 15 ECTS. The DCU modules cover advanced topics in artificial intelligence, such as machine translation and web search. They are assessed through continuous assessment and final exams, and the details of these assessments may vary from time to time. The grade in this module will equal the mean of the grades of two DCU modules. The learning outcomes of this module map closely to the learning outcomes of the DCU modules.

(Language of instruction: English)

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Learning Outcomes

1. Recognise and explain advanced artificial intelligence models in areas such as machine translation and web search.
2. State and explain use cases for such models, and models' strengths and weaknesses, including potential commercial application.
3. Implement such models, including the appropriate use of software libraries.
4. Evaluate such models both quantitatively and qualitatively.
5. Read and evaluate the scholarly literature and state of the art in such models.

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Assessments

This module's usual assessment procedures, outlined below, may be affected by COVID-19 countermeasures. Current students should check Blackboard for up-to-date assessment information.

Continuous Assessment (100%)

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Module Director

JAMES MCDERMOTT:

Research Profile

| Email

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Lecturers / Tutors

JAMES MCDERMOTT:  Research Profile

The above information outlines module DCU_CT558: "Further Topics in AI - Online - DCU" and is valid from 2021 onwards.

Note: Module offerings and details may be subject to change.

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Why Choose This Course?

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Career Opportunities

This innovative online MSc in Artificial Intelligence will equip the student with state-of-the-art knowledge and practical skills that are increasingly sought after in industry today.

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About University of Galway

Founded in 1845, we've been inspiring students for 178 years. University of Galway has earned international recognition as a research-led university with a commitment to top quality teaching.