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 NUI 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.

AI online logo for ICT Skillnet

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

Applications and Selections

How to apply: see https://www.ictskillnet.ie/training/msc-in-computer-science-artificial-intelligence/

Eligibility: in addition to the academic requirements, candidates must be working in private or commercial semi-state organisations in Republic of Ireland.

Send your CV showing your qualifications and experience to info@ictskillnet.ie. Candidates are encouraged to apply as early as possible. More information here.

Who Teaches this Course

Requirements and Assessment

Key Facts

Entry Requirements

This MSc is targeted at people currently working in industry who wish to significantly deepen their computing skills through a specialisation in Artificial Intelligence. Candidates must have a strong 2.2 Level 8 computer science degree or a strong 2.2 Level 8 science/engineering degree that provides extensive training in computing. 

Candidates who do not meet this requirement but are deemed by the programme director to have reached an equivalent standard will also be considered.

Candidates must be EU/EEA nationals or working in Ireland on an Irish Employment Permit.

Eligibility: in addition to the academic requirements, candidates must be working in private or commercial semi-state organisations in Republic of Ireland.


Additional Requirements

Duration

2 years, part-time

Next start date

September 2019

A Level Grades ()

Average intake

25

Closing Date

23 July 2019

NFQ level

Mode of study

Online, part-time.  

ECTS weighting

90

Award

CAO

Course code

MAO2

Course Outline

The MSc in Computer Science—Artificial Intelligence (online) is a 2-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 NUI Galway will also have the opportunity to choose from the following optional online modules on offer from Dublin City University.

  • Computer Vision;
  • Statistical Machine Translation;
  • Mechanics of Search.

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

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 (30 Credits)

Required CT5148: Programming and Tools for Artificial Intelligence - Online


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, cloud and GPU execution.
(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 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.
Assessments
  • Written Assessment (50%)
  • Continuous Assessment (50%)
Module Director
Lecturers / Tutors
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 2019 onwards.
Note: Module offerings and details may be subject to change.

Required CT5143: 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 task and provides in-depth coverage of important algorithms for tackling them. The learners get practice in selecting and applying these algorithms in applications, and evaluating and interpreting the results. Topics include: 1. Overview of Machine Learning & Major Categories of Task; 2. Supervised Learning Principles and Learning Decision Trees; 3. Instance-Based Learning and kNN; 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. Introduction to Reinforcement Learning.
(Language of instruction: English)

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.
Assessments
  • Written Assessment (70%)
  • Continuous Assessment (30%)
Module Director
Lecturers / Tutors
The above information outlines module CT5143: "Machine Learning - Online" and is valid from 2019 onwards.
Note: Module offerings and details may be subject to change.

Required CT5146: Introduction to Natural Language Processing - Online


Semester 1 | Credits: 5

Introduction to natural language processing, including foundations in linguistics, statistical analysis and applications.
(Language of instruction: English)

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.
Assessments
  • Written Assessment (50%)
  • Continuous Assessment (50%)
Module Director
Lecturers / Tutors
The above information outlines module CT5146: "Introduction to Natural Language Processing - Online" and is valid from 2019 onwards.
Note: Module offerings and details may be subject to change.

Required 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. 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
Assessments
  • Continuous Assessment (100%)
Module Director
Lecturers / Tutors
The above information outlines module CT5144: "Research Skills in Artificial Intelligence" and is valid from 2019 onwards.
Note: Module offerings and details may be subject to change.

Required 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; 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)

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
Assessments
  • Written Assessment (70%)
  • Continuous Assessment (30%)
Module Director
Lecturers / Tutors
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 2019 onwards.
Note: Module offerings and details may be subject to change.

Required CT5136: Data Visualisation - Online


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)

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
Assessments
  • Continuous Assessment (50%)
  • Computer-based Assessment (50%)
Module Director
Lecturers / Tutors
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 2019 onwards.
Note: Module offerings and details may be subject to change.

Year 2 (60 Credits)

Required 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 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)

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.
Assessments
  • Continuous Assessment (100%)
Module Director
Lecturers / Tutors
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 2019 onwards.
Note: Module offerings and details may be subject to change.

Required CT5131: Capstone Project and Thesis in Artificial Intelligence - Online


12 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%)
Module Director
Lecturers / Tutors
The above information outlines module CT5131: "Capstone Project and Thesis in Artificial Intelligence - Online" and is valid from 2019 onwards.
Note: Module offerings and details may be subject to change.

Optional CT5146: Introduction to Natural Language Processing - Online


Semester 1 | Credits: 5

Introduction to natural language processing, including foundations in linguistics, statistical analysis and applications.
(Language of instruction: English)

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.
Assessments
  • Written Assessment (50%)
  • Continuous Assessment (50%)
Module Director
Lecturers / Tutors
The above information outlines module CT5146: "Introduction to Natural Language Processing - Online" and is valid from 2019 onwards.
Note: Module offerings and details may be subject to change.

Optional CT5148: Programming and Tools for Artificial Intelligence - Online


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, cloud and GPU execution.
(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 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.
Assessments
  • Written Assessment (50%)
  • Continuous Assessment (50%)
Module Director
Lecturers / Tutors
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 2019 onwards.
Note: Module offerings and details may be subject to change.

Optional CT5143: 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 task and provides in-depth coverage of important algorithms for tackling them. The learners get practice in selecting and applying these algorithms in applications, and evaluating and interpreting the results. Topics include: 1. Overview of Machine Learning & Major Categories of Task; 2. Supervised Learning Principles and Learning Decision Trees; 3. Instance-Based Learning and kNN; 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. Introduction to Reinforcement Learning.
(Language of instruction: English)

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.
Assessments
  • Written Assessment (70%)
  • Continuous Assessment (30%)
Module Director
Lecturers / Tutors
The above information outlines module CT5143: "Machine Learning - Online" and is valid from 2019 onwards.
Note: Module offerings and details may be subject to change.

Optional CT5147: Knowledge Representation & Statistical Relational Learning - Online


Semester 2 | Credits: 5

Overview This module introduces students to formal Knowledge Representation and Statistical Relational Learning. Knowledge representation and reasoning are concerned with the efficient formal representation of information and its utilization for automated problem-solving tasks. Statistical Relational Learning is an area of Artificial Intelligence and Machine Learning concerned with the representation of, and reasoning and learning with, uncertain (probabilistic) and relational domain knowledge (such as graphs, web links or symbolic facts). Planned topics: 1. Foundations of knowledge representation 2. Propositional and first-order logic 3. Foundations of reasoning (deductive, inductive, abductive, probabilistic) 4. SAT and SMT 5. Logic programming 6. Probabilistic logics and uncertainty reasoning. 7. Parameter and structure learning in statistical-relational settings Requirements - Existing basic knowledge of Machine Learning
(Language of instruction: English)

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 statistical-relational settings
Assessments
  • Written Assessment (75%)
  • Continuous Assessment (25%)
Module Director
Lecturers / Tutors
The above information outlines module CT5147: "Knowledge Representation & Statistical Relational Learning - Online" and is valid from 2019 onwards.
Note: Module offerings and details may be subject to change.

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


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)

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.
Assessments
  • Written Assessment (70%)
  • Continuous Assessment (30%)
Module Director
Lecturers / Tutors
The above information outlines module CT5130: "Agents, Multi-Agent Systems and Reinforcement Learning - Online" and is valid from 2019 onwards.
Note: Module offerings and details may be subject to change.

Optional DCU_CA681: DCU - Statistical Machine Translation - Online


Semester 2 | Credits: 5

This course introduces the fundamentals of statistical machine translation. This is an optional module offered by Dublin City University; only available to students of the online MSC in Computer Science - Artificial Intelligence
(Language of instruction: English)

Learning Outcomes
  1. Discuss the challenges associated with machine translation
  2. Explain the noisy channel model underpinning statistical machine translation
  3. Demonstrate how a statistical translation model can be inferred from a parallel corpus of texts using unsupervised machine learning techniques
  4. Explain the concept of statistical language modelling and how it fits in to the basic SMT architecture
  5. Explain the concept of decoding and be in a position to implement a beam decoder
  6. Evaluate a statistical machine translation system using at least one automatic metric
  7. Demonstrate a knowledge of the state-of-the-art in statistical machine translation
  8. Train, test and evaluate MT systems using the open-source Moses toolkit
  9. Implement a language modeller (including smoothing) and a basic word aligner
Assessments
  • Written Assessment (50%)
  • Continuous Assessment (50%)
Module Director
The above information outlines module DCU_CA681: "DCU - Statistical Machine Translation - Online" and is valid from 2019 onwards.
Note: Module offerings and details may be subject to change.

Optional DCU_EE544: DCU - Computer Vision - Online


Semester 2 | Credits: 5

Computer vision applications have significantly expanded over the last decade and this core skill set is always in high demand by employers. This module will build on the basic concepts with a view to delving deeper into core computer vision, machine learning and deep learning topics. As well as examining traditional computer vision concepts (i.e. feature extraction and machine learning) a key focus of the module will be on deep learning as applied to computer vision. We will examine the core concepts behind deep learning for computer vision with a specific focus on Convolutional Neural Networks (CNN). Students will learn how to design and tune such networks in a range of practical applications and assignments. In addition we will examine a range of deep learning architectures ranging from AlexNet upto the current state of the art in this ever expanding field. Deep learning based computer vision forms the core of many of the recent developments in this field and has been widely adopted as a core AI tool by all the key industrial players such as Google, Facebook, IBM, Apple, Baidu ... as well as a wide range of highly innovative startups. All computer vision and deep learning concepts will be reinforced by guided practical work and case studies. This module is primarily aimed at those who aim to undertake research in computer vision or require a deeper understanding of the subject to address commercial computer vision development. Computer vision applications span a wide range of disciplines including industrial/machine vision, video data processing, biomedical engineering, healthcare, astronomy, imaging science, sensor technology, multimedia and enhanced reality systems. Please refer to the modules summary syllabus for a breakdown of the course content. This module will require basic programming skills. See the EE544 module website (http://www.eeng.dcu.ie/~whelanp/ee544/) for details on the computer vision & deep learning development environment.
(Language of instruction: English)

Learning Outcomes
  1. Recall, review and analyse the advanced theories, algorithms, methodologies and techniques involved in traditional and deep learning based computer vision.
  2. Illustrate their ability to comprehend and interpret issues relating to the design of advanced traditional and deep learning based computer vision
  3. Synthesize and evaluate the relevant merits of competing advanced computer vision techniques.
  4. Apply computer vision techniques in a range of application scenarios.
  5. Develop a deep understanding of the issues involved in the evaluating computer vision system implementation.
  6. Demonstrate the ability to implement a computer vision pipeline.
Assessments
  • Written Assessment (60%)
  • Continuous Assessment (40%)
Module Director
The above information outlines module DCU_EE544: "DCU - Computer Vision - Online" and is valid from 2019 onwards.
Note: Module offerings and details may be subject to change.

Optional 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 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)

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.
Assessments
  • Written Assessment (70%)
  • Continuous Assessment (30%)
Module Director
Lecturers / Tutors
The above information outlines module CT5145: "Deep Learning - Online" and is valid from 2019 onwards.
Note: Module offerings and details may be subject to change.

Optional CT5136: Data Visualisation - Online


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)

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
Assessments
  • Continuous Assessment (50%)
  • Computer-based Assessment (50%)
Module Director
Lecturers / Tutors
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 2019 onwards.
Note: Module offerings and details may be subject to change.

Optional 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; 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)

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
Assessments
  • Written Assessment (70%)
  • Continuous Assessment (30%)
Module Director
Lecturers / Tutors
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 2019 onwards.
Note: Module offerings and details may be subject to change.

Optional 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. 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
Assessments
  • Continuous Assessment (100%)
Module Director
Lecturers / Tutors
The above information outlines module CT5144: "Research Skills in Artificial Intelligence" and is valid from 2019 onwards.
Note: Module offerings and details may be subject to change.

Why Choose This Course?

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. 

Who’s Suited to This Course

Learning Outcomes

 

Work Placement

Study Abroad

Related Student Organisations

Course Fees

Fees: EU

€3,975 p.a. 2019/20

Fees: Tuition

€3,919 p.a. 2019/20

Fees: Student levy

€56 p.a. 2019/20

Fees: Non EU

Candidates must be EU/EEA nationals or working in Ireland on an Irish Employment Permit

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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 

Find out More

For more information email info@ictskillnet.ie or visit www.ictskillnet.ie

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