Computer Science—Artificial Intelligence (MSc, online, part-time)

Applications and Selections

Who Teaches this Course

Heike Schmidt-Felzmann

Mr Enda Barrett

BSc

View Profile

Prof Peter Paul Buitelaar

PhD.

Professor in Data Analytics
Data Science Institute
University of Galway
View Profile

Prof Edward Curry

B.Sc., Ph.D.

View Profile

DR CONOR HAYES

B.A., M.Sc., Ph.D.

View Profile

Dr Enda Howley

B.Sc, Ph.D

Senior Lecturer
INFORMATION TECHNOLOGY
SCHOOL OF ENGINEERING
& INFORMATICS
IT BUILDING
View Profile

Prof Michael Madden

B.E., Ph.D., M.I.E.I.

View Profile

Dr. John Mc Crae

M.Sc., PhD

View Profile

Dr James Mc Dermott

B.Sc., PhD

Lecturer Above The Bar
IT Building 441
University of Galway
Galway
Ireland
View Profile

Dr Patrick Mannion

B.Eng, Phd

Lecturer Above the Bar / Assistant Professor in Computer Science
Computer Science Building
University of Galway
View Profile

Mr. Colm O'Riordan

B.Sc, M.Sc,

View Profile

PROF David O'Sullivan

B.Sc., Dip.Mech.Eng., M.E.D., Ph.D.

View Profile

Requirements and Assessment

Glossary of Terms

Year 1 (90 Credits)

OptionalCT5148: Programming and Tools for Artificial Intelligence - Online

CT5148: Programming and Tools for Artificial Intelligence - Online

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

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 numerical Python libraries for manipulation, input/output, visualisation of numerical data using Numpy array types.
4. Use essential tools for AI, including libraries for data gathering, numerical computing, machine learning, combinatorial programming, and modelling networks.
5. 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

•  DEIRDRE KING 🖂
  
•  GERALDINE HEALY 🖂
  
•  JAMES MCDERMOTT 🖂
  

Reading List

1. "A Whirlwind Tour of Python," by Jake Vanderplas
2. "Think Python 2nd edition" by Allen B. Downey

OptionalCT5170: Principles of Machine Learning - Online

CT5170: Principles of Machine Learning - Online

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

1. Define Machine Learning and explain what major categories of learning tasks 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, 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.

Assessments

• Written Assessment (70%)
• Continuous Assessment (30%)

Teachers

•  DEIRDRE KING 🖂
  
•  GERALDINE HEALY 🖂
  
•  Ihsan Ullah 🖂
  

OptionalST5001: Statistics for Artificial Intelligence

ST5001: Statistics for Artificial Intelligence

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)

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.

Assessments

• Continuous Assessment (100%)

Teachers

•  COLLETTE MCLOUGHLIN 🖂
  

OptionalCT5152: Artificial Intelligence and Ethics - Online

CT5152: Artificial Intelligence and Ethics - Online

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 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

1. Demonstrate competence in using specialist ethical concepts
2. Identify and summarise important ethical concerns related to the design, use and societal impact of Artificial Intelligence
3. Apply relevant theoretical models from the ethical, legal and social science literature to identified ethical concerns regarding AI.
4. 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.
5. Demonstrate the ability to communicate insights from divergent perspectives on ethical concerns coherently and concisely.
6. Demonstrate the ability to connect learnings with personal and professional experiences.

Assessments

• Continuous Assessment (100%)

Teachers

•  HEIKE SCHMIDT-FELZMANN 🖂
  
•  DEIRDRE KING 🖂
  
•  GERALDINE HEALY 🖂
  

Reading List

1. "Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence" by Kate Crawford
   Publisher: Yale University Press
2. "The Oxford Handbook of Ethics of AI" by Markus Dubber, Frank Pasquale, Sunit Das (eds.)
   Publisher: OUP
3. "Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor" by Virginia Eubanks
   Publisher: St Martin's Press
4. "Ethics of Artificial intelligence" by Matthew Liao (ed.)
   Publisher: OUP
5. "Robot Ethics 2.0: From Autonomous Cars to Artificial Intelligence" by Patrick Lin, Keith Abney, Ryan Jenkins (Editors)
   Publisher: Oxford University Press
6. "Privacy in Context: Technology, Policy, and the Integrity of Social Life," by Helen Nissenbaum
   Publisher: Stanford University Press
7. "The Oxford Handbook of Digital Ethics" by Carissa Veliz (ed.)
   Publisher: OUP
8. "Privacy as Trust: Information Privacy for an Information Age" by Ari Waldman
   Publisher: Cambridge University Press
9. "The Age of Surveillance Capitalism" by Shoshanna Zuboff
   Publisher: Profile Books

OptionalCT5144: Research Skills in Artificial Intelligence

CT5144: Research Skills in Artificial Intelligence

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)

Learning Outcomes

1. Develop 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. Share and discuss state of the art in AI research
5. Publish literature review for a research topic in Artificial Intelligence

Assessments

• Continuous Assessment (100%)

Teachers

•  DAVID O'SULLIVAN 🖂
  
•  DEIRDRE KING 🖂
  
•  GERALDINE HEALY 🖂
  

OptionalCT5146: Introduction to Natural Language Processing - Online

CT5146: Introduction to Natural Language Processing - Online

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

1. Ability to explain the various levels of linguistic structure relevant to NLP.
2. Ability to use standard algorithms for basic NLP analysis
3. Gain practical knowledge of and experience in the use of NLP toolkits
4. Ability to explain a selection of theoretical principles behind core NLP applications.
5. 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

•  DEIRDRE KING 🖂
  
•  PETER PAUL BUITELAAR 🖂
  
•  GERALDINE HEALY 🖂
  
•  JAMES MCDERMOTT 🖂
  
•  LORNA MOYLAN 🖂
  
•  John McCrae 🖂
  

OptionalCT5153: Information Retrieval - Online

CT5153: Information Retrieval - Online

Semester 1 | Credits: 5

The course introduces the main theories and techniques in the domain of information retrieval.
(Language of instruction: English)

Learning Outcomes

1. Explain the main models used in information retrieval.
2. Explain the factors involved in designing and analysing weighting schemes
3. Be able to choose suitable data structures and algorithms for building IR systems
4. Be able to explain the main ideas and approaches used in web search
5. Explain the main ideas and approaches used in recommender systems
6. Explain the concepts in applying learning mechanisms in information retrieval

Assessments

• Written Assessment (70%)
• Continuous Assessment (30%)

Teachers

•  COLM O'RIORDAN 🖂
  
•  DEIRDRE KING 🖂
  
•  GERALDINE HEALY 🖂
  

OptionalCT5131: Capstone Project and Thesis in Artificial Intelligence - Online

CT5131: Capstone Project and Thesis in Artificial Intelligence - Online

15 months long | Credits: 30

Capstone Project and Minor Thesis in Artificial Intelligence (30 ECTS)
(Language of instruction: English)

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

Assessments

• Research (100%)

Teachers

•  DEIRDRE KING 🖂
  
•  GERALDINE HEALY 🖂
  
•  Jamal Nasir 🖂
  

OptionalCT5136: Data Visualisation - Online

CT5136: Data Visualisation - Online

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

1. Describe the basic design principles underlying human perception, color theory and narrative
2. Analyse the effectiveness of different visual elements in communicating analytical information
3. Select the best visualisation strategy to use for different exploratory and explanatory scenarios
4. Execute different types of visualisations for use in various exploratory and explanatory scenarios
5. Carry out basic data preprocessing and wrangling necessary to produce effective visualisations
6. Discuss the ethical requirements of representing data and information truthfully when creating a visualisation
7. Critically evaluate data visualisations produced by others

Assessments

• Written Assessment (65%)
• Continuous Assessment (35%)

Teachers

•  DEIRDRE KING 🖂
  
•  CONOR HAYES 🖂
  
•  GERALDINE HEALY 🖂
  

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.

OptionalCT5150: Tools and Techniques for Large Scale Data Analytics - Online

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

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; Relevant frameworks from the Apache ecosystem (such as 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.
(Language of instruction: English)

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 storage, 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 event processing

Assessments

• Written Assessment (70%)
• Continuous Assessment (30%)

Teachers

•  MATTHIAS NICKLES 🖂
  
•  DEIRDRE KING 🖂
  
•  GERALDINE HEALY 🖂
  

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, Inc."
3. "Large-Scale Data Analytics" by Aris Gkoulalas-Divanis,Abderrahim Labbi
   ISBN: 1461492424.
   Publisher: Springer Science & Business Media

OptionalCT5145: Deep Learning - Online

CT5145: Deep Learning - Online

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

1. Explain key Machine Learning concepts that relate to Deep Learning
2. Explain the operation of feed-forward neural networks and the back-propagation algorithm
3. Describe, implement and apply key features of deep neural networks
4. Implement NNs for supervised machine learning tasks, from first principles and (separately) using modern libraries and frameworks
5. 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.
6. Discuss ethical issues, limitations, and emerging trends in deep learning.

Assessments

• Written Assessment (60%)
• Continuous Assessment (40%)

Teachers

•  MICHAEL MADDEN 🖂
  
•  JAMES MCDERMOTT 🖂
  

OptionalCT5130: Agents, Multi-Agent Systems and Reinforcement Learning - Online

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

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

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. Formulate a decision making problem as a Markov decision process (MDP)
5. Apply reinforcement learning algorithms to learn policies for MDPs
6. Conduct experiments to determine appropriate hyperparameters for a reinforcement learning algorithm

Assessments

• Written Assessment (70%)
• Continuous Assessment (30%)

Teachers

•  DEIRDRE KING 🖂
  
•  ENDA HOWLEY 🖂
  
•  GERALDINE HEALY 🖂
  
•  PATRICK MANNION 🖂
  

OptionalCT5188: Knowledge Representation - Online

CT5188: Knowledge Representation - Online

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

1. Explain the fundamental principles of knowledge representation and reasoning
2. Describe and use syntax and semantics of important formal logics
3. Explain and be able to use fundamental types of reasoning and logic frameworks
4. Model application domains using logic languages and relational knowledge representation formats
5. Explain and apply selected Machine Learning models in the context of knowledge representation and reasoning

Assessments

• Written Assessment (70%)
• Continuous Assessment (30%)

Teachers

•  MATTHIAS NICKLES 🖂
  
•  DEIRDRE KING 🖂
  
•  GERALDINE HEALY 🖂
  

Reading List

1. "Knowledge Representation and Reasoning" by Ronald J. Brachman, Hector J. Levesque
   Publisher: Elsevier/Morgan Kaufmann

OptionalPH504: High Performance Computing and Parallel Programming

PH504: High Performance Computing and Parallel Programming

Semester 2 | Credits: 5

Assessments

• Continuous Assessment (100%)

Teachers

•  NICHOLAS DEVANEY 🖂
  
•  SIMON WONG 🖂
  
•  REBECCA NOLAN 🖂
  

OptionalDCU_CT558: Further Topics in AI - Online - DCU

DCU_CT558: Further Topics in AI - Online - DCU

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)

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.

Assessments

• Continuous Assessment (100%)

Teachers

•  DEIRDRE KING 🖂
  
•  GERALDINE HEALY 🖂
  
•  Jamal Nasir 🖂