This AI Security module equips students with offensive and defensive techniques to secure AI architectures, generative models, and AI-driven applications. Spanning background fundamentals, practical labs, and policy frameworks, the course ensures graduates can identify and mitigate security flaws while complying with emerging regulations and operating AI systems securely at scale.

University College Dublin

Computing and IT Skills 

IT and Computer Science

This AI Security module equips students with offensive and defensive techniques to secure AI architectures, generative models, and AI-driven applications. Spanning background fundamentals, practical labs, and policy frameworks, the course

AI Security: Offensive, Defensive, and Operational Best Practices

The module covers what is meant by learning from data and using patterns in data to learn from it as long as we have enough data from which to learn. The module will look at how to learn from data, using basic techniques and trialling them on data sets, and will look at algorithms to make machine learning better.

University of Limerick

Data Science 

The module covers what is meant by learning from data and using patterns in data to learn from it as long as we have enough data from which to learn.

Artificial Intelligence And Machine Learning

This module aims to train students in two critical areas:1. Using AI to Support Investigations by:<ul><li>Exploit ML-based techniques such as NLP (Natural Language Processing), anomaly detection, OSINT (Open Source Intelligent scraping) to analyze digital evidence (network logs, social media, financial records).</li><li>Apply AI-powered workflows in domains like fraud detection, insider threat analysis, cybersecurity forensics, and eDiscovery.</li></ul>2. Investigating AI/Generative AI Systems:<ul><li>Audit AI models (bias detection, adversarial vulnerabilities, explainability).</li><li>Conduct forensic analysis on Generative AI outputs (deepfakes, malicious text).</li></ul>This module integrates theoretical foundations (algorithms, interpretability methods, regulatory knowledge) with hands-on practice (labs, case studies, project work) to prepare students for next generation roles in law enforcement, digital forensics, compliance, and AI governance.

Automation and AI 

This module aims to train students in two critical areas:1. Using AI to Support Investigations by:Exploit ML-based techniques such as NLP (Natural Language Processing), anomaly detection, OSINT (Open Source Intelligent

Artificial Intelligence Forensics and Investigations

This module introduces the elements of the data analytics workflow, including data cleaning, feature extraction and feature selection, predictive and descriptive modelling, and deployment of models. The role of data visualisation in the data analytics process is discussed as well. The module involves practice with a state-of-the-art data analytics software platform.

This module introduces the elements of the data analytics workflow, including data cleaning, feature extraction and feature selection, predictive and descriptive modelling, and deployment of models. The role of data

Data Analytics

100% online - Data Analytics with R This module will cover an introduction to programming in R and RStudio for data analytics assuming no prior experience. The module will cover data wrangling, data visualisation, summarising data, and simple statistical modelling in R. Students will learn about reproducible research, coding and analytics practices, and be given an introductory overview of RShiny dashboards.

100% online - Data Analytics with R This module will cover an introduction to programming in R and RStudio for data analytics assuming no prior experience. The module will cover

Data Analytics with R

The role of the test engineer is becoming ever more diverse with activities that link into design and IC fabrication. In this module, the role of test and test development engineering will be introduced and discussed in relation to their integration within a modern semiconductor company. The actions undertaken within the role of test will be discussed and the types of tests to be undertaken to identify the operation of digital ICs will be elaborated.This is a standalone module for engineers working in the semiconductor industry. The specific focus of the module is in digital Integrated Circuit (IC) test.This module is offered to practicing engineers working in digital Integrated Circuit (IC) test and test development activities. The module will be taught using a blended approach using on-campus face-to-face teaching and online using an appropriate Learning Management System (LMS).The module will be run outside the normal Central Scheduling System and will be based on an initial on-campus day in week 1, online activities in weeks 2 to 14, and a final on-campus day in week 15.

The role of the test engineer is becoming ever more diverse with activities that link into design and IC fabrication. In this module, the role of test and test development

Digital Test and Measurement

This module will investigate the increasing digitalisation of business processes, from designÂ through to process execution. Process modelling will be introduced and the use ofÂ visualisation techniques in supporting process improvement will be established. The module will establish a robust process for understanding the digitisation requirements, paths to digitising the process and methods of process visualisation andÂ analysis using platforms like SAS JMP Pro Software. Robotic Process Automation (RPA) will also be considered with exposure to Software platforms such as Blue Prism.

This module will investigate the increasing digitalisation of business processes, from designÂ through to process execution. Process modelling will be introduced and the use ofÂ visualisation techniques in supporting process

Digitalisation Of Process

This course will teach students how criminally derived money works in both the real world and in cyberspace, and how to investigate both. The module will look at how money and assets are used and abused by criminals and terrorists. During the term, the course will examine the major types of fraud, including VAT Carousel Fraud (responsible for the loss of between â‚¬43-53 Billion in the EU, annually) and we will examine methods used to launder criminal proceeds. Students will make extensive use of Open-source Intelligence (OSINT) methods of particular relevance to investigators.

This course will teach students how criminally derived money works in both the real world and in cyberspace, and how to investigate both. The module will look at how money

Financial Investigation Techniques - Following the Money

Applications open 1st October 2024This module introduces students to the theory behind simple and compound machines, reliability of a system, gearing and gear ratio theory, spring decision and design criteria.

Applications open 1st October 2024This module introduces students to the theory behind simple and compound machines, reliability of a system, gearing and gear ratio theory, spring decision and design criteria.

Machine Design

Learners are introduced to automation and automated systems. Items such as sensors, motors, pneumatics, Programmable Logic Controller (PLC) programming and robotics are discussed. Material handling and storage are also considered.

Learners are introduced to automation and automated systems. Items such as sensors, motors, pneumatics, Programmable Logic Controller (PLC) programming and robotics are discussed. Material handling and storage are also considered.

Automation 1

The Manufacturing Technology module introduces engineering materials including metals, ceramics, polymers and composite materials, manufacturing processes, both traditional and recently developed and electronic manufacturing technologies.

The Manufacturing Technology module introduces engineering materials including metals, ceramics, polymers and composite materials, manufacturing processes, both traditional and recently developed and electronic manufacturing technologies.

Manufacturing Technology

Applications open 1st October 2024The aim of the Automation 2 module is to give you some important insights into the application of automation in industry as detailed in the module aims of Automation 1.

Applications open 1st October 2024The aim of the Automation 2 module is to give you some important insights into the application of automation in industry as detailed in the module

Automation 2

Topics covered in this short course include:Introducing data communications; Defining key terms and concepts 	What is data and datafication? Examining the landscape of open data, public data and critically the process of datafication. Show how datasets and databases are built.	Data modelling and algorithms. How data is classified, categorised, and modelled; the technical process of algorithmic decision-making and developing students' ability to analyse and evaluate potential limitations in the process.	Understanding data-driven methodologies: Exploring the most common ways data is used and developed in research and highlighting means of evaluating the quality and limitations of data and different data-driven methodologies.	Data and disciplines: Different approaches to data in science, social science, politics, business and more.	Data and the media: Developing a critical awareness of good and bad practices regarding the mediation of data-driven information	Data and regulation: Exploring the landscape of Irish and EU regulation around data and considering specific implications for data communications.

Topics covered in this short course include:Introducing data communications; Defining key terms and concepts	What is data and datafication? Examining the landscape of open data, public data and critically the

Introduction to Data Communications

Topics covered on this microcred include:Principles of data communication. Develop a better understanding of the core principles around communicating data-driven information to the general public. Additionally, we consider implications for communicating with different types of audiences.	Data gathering and analysis. Introduction to open and public data; Overview of approaches to sourcing and reviewing datasets to establish what stories emerge. 	Establish connections within and between datasets. Understanding relationships between different types of data-driven research, integrating complex data into stories, and communicating conflicting data. 	How to build narratives or stories from data. Applying knowledge gained to develop a range of narratives or stories about data on smaller or larger scales, small to large scale.	Collaborative data project based on our learner's disciplines, areas of work or interest.This module builds on from module An Introduction to Data Communication by further exploring how to prepare and extract key features of large datasets and use them to tell stories for different types of audiences. Students will also learn key concepts and techniques behind data perception and visualisation, as well as attaining basic proficiency in some associated software tools.

Topics covered on this microcred include:Principles of data communication. Develop a better understanding of the core principles around communicating data-driven information to the general public. Additionally, we consider implications for

Advanced Data Storytelling

This module will introduce practical skills including a landscape of quantum computing systems, algorithms, tools, and applications. Attendees will be equipped to assess technological evolution, define use-cases, and create a roadmap for the necessary technical and skills development activities required for their domains and organisations. The module is tailored in a flexible modular format and is delivered in a blended mechanism.

This module will introduce practical skills including a landscape of quantum computing systems, algorithms, tools, and applications. Attendees will be equipped to assess technological evolution, define use-cases, and create a

Quantum Programming

Quantum Programming Foundations

This module is designed to provide learners with a comprehensive understanding of the implications of advancements in AI for organisations and society.

This module is designed to provide learners with a comprehensive understanding of the implications of advancements in AI for organisations and society. 

Societal Impact of Artificial Intelligence

This module at University of Galway, will introduce the concept of Machine Learning and look at some interesting applications. The theoretical aspects of the subcategories of Machine Learning (Supervised Learning, Unsupervised Learning, Semisupervised Learning, Reinforcement Learning, and Deep Learning) will be studied in detail as well as common terminology associated with each. The second half will apply these machine learning techniques to problems in natural language, looking at the problems of text classification, annotation, translation and knowledge extraction. In addition, we will cover some useful linguistic fundamentals for understanding the challenges of natural language processing.

This module at University of Galway, will introduce the concept of Machine Learning and look at some interesting applications. The theoretical aspects of the subcategories of Machine Learning (Supervised Learning,

Machine Learning and Natural Language Processing

The operation of modern semiconductor devices is underpinned by a good knowledge of the physics of solid-state materials and electronics. This micro-credential is motivated by the need to link physical models with modern device operation. The micro-credential uses basic quantum mechanical physical principles to explain the properties of materials of interest in electronic engineering practice. A knowledge of the bonding between atoms is essential to understand the behaviour of solids and their electronic properties, which distinguish conductors from semiconductors and insulators. These impact on the electronic properties of materials, which in turn control the operation of electronic devices currently used and under development. Building on this foundation of solid-state physics the micro-credential introduces the student to the basic parameters which control the behaviour of electronic devices, such as diodes, bipolar transistors, MOSFETs and lasers. The phenomena which occur in non-idealized devices are studied and the behaviour of real devices is examined in the light of these phenomena.

The operation of modern semiconductor devices is underpinned by a good knowledge of the physics of solid-state materials and electronics. This micro-credential is motivated by the need to link physical

Solid State Electronic & Semiconductor Devices (EEN1049)

The New Technology and Responsible Innovation micro-credential addresses urgent and fascinating questions regarding technology and the ethical challenges arising from its impact on our everyday lives, work environment, and society, especially in terms of privacy and responsibility.The micro-credential will guide students through a multifaceted analysis of technology. We will start by analysing how technologies shape our perception and understanding of workplace and relationships. We identify and discuss some of the main ethical questions that recent technological development has raised, such as should moral values be integrated in the design of technologies? If so, how do we achieve that? Moreover, how do we make sure that we introduce and use technologies in a responsible way? And what are the benefits and costs of the growing importance of AI technology? How should we evaluate possible ethical consequences, such as misdiagnosing patients, increasing misinformation, and spreading prejudices? Who is responsible for the harm caused? The micro-credential will also explore possible strategies to regulate the impact of technologies, the benefits they provide, and the risks they impose, and discuss which ethical principles should guide the design, introduction, and use of technologies and the moral virtues that responsible designers and users should develop.

Leadership and Management 

Business and Management

The New Technology and Responsible Innovation micro-credential addresses urgent and fascinating questions regarding technology and the ethical challenges arising from its impact on our everyday lives, work environment, and society,

New Technology and Responsible Innovation

This module adopts an applied learning approach to understanding embedded systems, theÃ¢Â€Â Internet-of-things (IoT) and the cyber-physical systems (sensors, control boards) necessary forÃ¢Â€Â data acquisition in business environments. The aim of this module is to enable the learner to programme standard ICT Boards, I/O, sensorsÃ¢Â€Â and gateways in order to collect time-series data streams. Furthermore, the application of dataÃ¢Â€Â stream analysis at the Board/Gateway level (edge computing) will be explored. Robotic ProcessÃ¢Â€Â Automation (RPA) will also be considered with exposure to Software platforms such as BlueÃ¢Â€Â Prism

This module adopts an applied learning approach to understanding embedded systems, theÃ¢Â€Â Internet-of-things (IoT) and the cyber-physical systems (sensors, control boards) necessary forÃ¢Â€Â data acquisition in business environments. The a

Introduction To Cyber-Physical Systems & Internet of Things

This module will cover an introduction to programming in R and RStudio for professionals working in the wind energy sector, assuming no prior coding experience. The module will cover data wrangling, data visualisation, summarising data, and simple statistical modelling in R. Students will learn about reproducible research, coding and analytics practices, and be given an introductory overview of RShiny dashboards for data generated in wind energy applications.

This module will cover an introduction to programming in R and RStudio for professionals working in the wind energy sector, assuming no prior coding experience.   The module will cover data

Introduction to Data Analytics with R for Wind Energy

This module adopts an applied learning approach to identify opportunities and work with dataÃ‚Â through the lens of the relational database model. The aim of this module is to introduce the learner to standard business systems to collect andÃ‚Â interpret datasets for data-driven intelligence. Therefore, learners will develop an understandingÃ‚Â of the need to design and develop database systems, collect, clean, visualise and interpret dataÃ‚Â rooted in best data analysis practice.

This module adopts an applied learning approach to identify opportunities and work with dataÃ‚Â through the lens of the relational database model. The aim of this module is to introduce

Introduction To Data Analytics/Visualisation & Machine Learning

This module will provide an introduction to the core mathematics and core programming skills required in machine learning. On the successful completion of this module participants will be able to:Use Python to implement standard programming constructs. 	Understand the use of Calculus, Linear Algebra and Probability Theory in machine learning applications	Apply Python to simple machine learning models and data visualisations.

This module will provide an introduction to the core mathematics and core programming skills required in machine learning. On the successful completion of this module participants will be able to:Use

Introduction to Scientific Computing in Artificial Intelligence

This microcred will introduce you to machine vision and image processing principles. Key topics such as linear image processing, feature detection and essential object detection are introduced.Practical examples of these techniques are included in the laboratories for this module to increase student engagement with this material. This short course is a precursor to advanced vision modules.

This microcred will introduce you to machine vision and image processing principles. Key topics such as linear image processing, feature detection and essential object detection are introduced.Practical examples of these

Machine Vision and Image Processing

Topics covered include:Basic Text Processing: Regular Expressions, Word Tokenization, Normalization, Stemming and Lemmatization, and Sentence Segmentation. 	String Similarity: Minimum Edit Distance, Backtrace and Alignment, Weighted Minimum Edit Distance, Phonetic Matching, Real-world applications (record de-duplication).	N-gram Language Models: Introduction to N-grams, Estimating N-gram Probabilities, N-grams Evaluation and Perplexity, Generalization and Zeros, Add-One (Laplace) Smoothing, (Interpolation, Good Turing Smoothing, Kneser Ney Smoothing), Google Books N-gram Corpus, Zipf's law.	Spelling Correction: Introduction to the task of Spelling Correction, The Noisy Channel Model of Spelling, Real Word Spelling Correction, Peter Norvig's Spell Checker, State of the Art Systems.	Text Classification: Introduction to the task of text classification, Introduction to Naive Bayes, Formalizing the Naive Bayes Classifier, Naive Bayes Learning, Naive Bayes Relationship to Language Modeling, Precision, Recall, and the F measure, Text Classification Evaluation (micro; macro averaging), Practical Issues in Text Classification, Manual labelling tools and techniques (Amazon Mechanical Turk, brat, doccano, inception).	Sentiment Analysis: Introduction to the task of Sentiment Analysis, the baseline algorithm for Sentiment Analysis (tokenization, feature extraction, classification), Sentiment Lexicons, Learning Sentiment Lexicons, and Other Sentiment Tasks (aspects, attributes, targets).

Topics covered include:Basic Text Processing: Regular Expressions, Word Tokenization, Normalization, Stemming and Lemmatization, and Sentence Segmentation.	String Similarity: Minimum Edit Distance, Backtrace and Alignment, Weighted Minimum Edit Distance, 

Natural Language Processing: An Introduction

This course is designed and delivered by the Innovation Value Institute, Maynooth University's Digital Transformation and Innovation Research Centre. The micro-credential provides participants with the knowledge, skills, and confidence to help build a sustainable and successful organisational digital transformation programme. The course explores a range of topics such as: <ul><li>the fundamentals of digital transformation;</li><li>approaches to change leadership, effective change management, and organisation capability improvement;</li><li>cultural, structural, and technology dynamics of digital transition;</li><li>improving the business value outcomes of transformation in organisations;</li><li>major benefits and challenges associated with the management and execution of digital transformation initiatives.</li></ul>This course is suitable for professionals across multiple industries and at various career stages: <ul><li>new career entrants wishing to develop skills and knowledge;</li><li>mid-career professionals seeking to enhance their digital leadership capabilities;</li><li>senior managers wishing to use digital to build operational excellence. </li></ul>

This course is designed and delivered by the Innovation Value Institute, Maynooth University's Digital Transformation and Innovation Research Centre. The micro-credential provides participants with the knowledge, skills, and confidence to

LEADING DIGITAL TRANSFORMATION IN ORGANISATIONS

In recent years, nanoelectronics has emerged as a revolutionary and dynamic field encompassing several disciplines such as engineering, physics, chemistry, and biology. The rapid advancements in nanoscale technology have ignited significant excitement among researchers and experts worldwide. With the ability to manipulate matter at an atomic scale, nanoelectronics holds tremendous potential for driving groundbreaking innovations across a broad spectrum of applications ranging from chemical processes to electronic devices to optical technologies and even medical advancements. The prospects offered by engineering at the nanoscale are simply remarkable. By exploiting unique properties exhibited by materials on such a small size range, scientists can create novel structures with enhanced functionalities that were previously unimaginable. As we delve deeper into this exciting realm of research and development, there is no doubt that many game-changing breakthroughs await us.

In recent years, nanoelectronics has emerged as a revolutionary and dynamic field encompassing several disciplines such as engineering, physics, chemistry, and biology. The rapid advancements in nanoscale technology have ignited

Nanoelectronics Technology (EEN1003)

This microcredential will equip students with a comprehensive grasp of the telecommunications industry, as well as the significance of photonic technologies in the operation of the heterogeneous broadband networks. Building on the primitive knowledge on optical communications, this course delves into the demands of various network segments, addressing both technological and business/cost considerations, and how they are fulfilled. A series of case scenarios will be presented, providing the students with a practical knowledge of network design, operation and performance metrics.

This microcredential will equip students with a comprehensive grasp of the telecommunications industry, as well as the significance of photonic technologies in the operation of the heterogeneous broadband networks. Building

Automation and AI

Microcreds