Artificial Intelligence - MSc (Online)

During this programme, you will:

• Acquire knowledge and skills in the application of Artificial Intelligence and Machine Learning.

• Explore relevant subjects in a self-driven manner, with expert support from module leaders and moderators.

• Gain an understanding of the key principles and methods in Machine Learning, Natural Language Processing or Computer Vision.

• Benefit from a mixed learning process with an emphasis on practical applications.

Key Information:

• Complete part-time over two years

• Delivered fully online

• Modules taught during autumn, spring and summer semesters

• Option to exit after first autumn semester with Certificate in Artificial Intelligence

• Master's programme officially starts in spring semester year 1

• Choose a specialised stream in Year 2

• Submit dissertation or development project in final semester

• Modules with (M) beside them can be taken as independent micro-credential

You will learn through a blend of:

• Lectures and hands-on activities

• Reflective practice and guided research

• Regular feedback from faculty and peers

Part-time considerations:

• Designed for working professionals

• Recorded evening lectures

• Online forum-based collaboration with peers

• Timetable provided after registration

Year 1

Autumn Semester

Certificate in Artificial Intelligence

• Introduction to Scientific Computing for AI (M) (CE4021) Introduces the core mathematics and core programming skills required in machine learning. Using a number of E-tivities you will hone your Python coding skills as well as your knowledge and skills in Calculus, Linear Algebra and Probability Theory as the three core areas of mathematics that underpin machine learning.

• Introduction to Deep Learning and Frameworks (CE4031) Previews the exciting possibilities that modern machine learning offers, introducing you to the core methods used in machine learning and state-of-the-art networks, such as Convolutional Neural Networks.

Exit option here

Spring Semester

Master’s programme start

• Artificial Intelligence and Machine Learning (M) (CE6002) Introduces the core concepts in machine learning and familiarising you with the theory that underpins statistical machine learning. Provides important insights into why and when machine learning is possible, and how to ensure the best performance possible.

• Data Analytics (M) (CS5062) Introduces a large number of practical skills used in machine learning, including approaches to pre-processing data, using this data to train various machine learning algorithms, and methods to visualise the data and the performance of your machine learning models.

Summer Semester

• Advanced Topics Seminars and Project Specification (CS6163) Introduces a number of advanced topics through seminars (commencing in the Spring semester) to help you decide on your topic of interest for your project. You will also learn about the crucial research methods required to successfully conduct a Master’s level research project and write a literature review on the topic of your choice. Note: This module begins in Week 1 of the Spring semester with a number of workshops and seminars, but all graded elements are due in the summer semester.

• Risk, Ethics, Governance and Artificial Intelligence (IN5103) A crucial element of your education as a responsible AI engineer, this module will introduce you to the risks and ethical issues associated with Artificial Intelligence.

Year 2

Choose one of three specialisation streams

Modern Machine Learning Stream:

Autumn Semester

• Machine Learning Applications (ET5003) Introduces you to advanced machine learning models and applications, including Natural Language Processing and probabilistic approaches to machine learning.

• Machine Vision (CE6003) Covers traditional methods of machine vision, as well as act as an introduction to the exciting area of deep learning, which has driven many of the most recent innovations in machine vision.

Spring Semester

• Deep Learning (CS5004) Takes an in-depth look at deep learning theory and practice. You will learn about the most important deep neural network architectures as well as the most important deep learning frameworks. You will then apply your newfound knowledge to a number of sample applications.

• Artificial Intelligence and Data Science Ecosystems: Theory and Practice (CS6512) Shows the two opposite sides of machine learning practice. On the one side, you will take a closer look at the algorithms that drive machine learning. On the other side, you will see how you can leverage these algorithms in AI workflows whilst minimising the coding effort through a model-driven design approach.

Natural Language Processing Stream:

Autumn Semester

• Natural Language Processing: An Introduction (MN5001) Introduces the world of Natural Language Processing (NLP), this module covers the fundamentals of statistical NLP, and its techniques and applications with a foundational approach

• Information Retrieval (MN5151) Introduces students to the fields of Information Retrieval, Information Extraction, and Semantic Web. The module will cover a blend of fundamental concepts and current tools, techniques, and technologies used in modern information retrieval systems.

Spring Semester

• Advanced Natural Language Processing (MN5002) Covers advanced level topics in natural language processing, with a focus on deep learning-based approaches. These include text classification, synthetic parsing, part of speech tagging, named-entity recognition, coreference resolution, and machine translation.

• Natural Language Understanding (MN5162) Introduces students to the field of Natural Language Understanding and related topics including sentiment analysis, relation extraction, natural language inference, semantic parsing, question answering, language generation, and conversational agents.

Computer Vision Stream:

Autumn Semester

• Deep Learning for Computer Vision (CE5021) Discusses the key computer vision tasks of image classification, object detection, semantic segmentation and facial recognition in detail, along with fundamental concepts in the design and structure of deep neural networks. Students gain a full understanding of how to design and build networks for their own applications.

• Machine Vision and Image Processing (CE5011) Introduces students to the principles of Machine Vision & Image Processing. Key topics such as linear image processing, feature detection and basic object detection are introduced with practical examples of these techniques.

Spring Semester

• Geometric Computer Vision (CE5002) Geometric computer vision is the process of determining the structure of the environment, the position, orientation and movement of the camera with respect to the environment, through the analysis of camera image streams. Students will gain a practical understanding of its use in mobile robotics, vehicle autonomy and augmented reality.

• Intelligent Visual Computing & Applications (CE5012) Focuses on applications of Deep-learning to important Computer Vision applications including Facial Recognition and 3D reconstruction. The use of transformer networks to build state-of-the art computer vision system is also discussed.

Summer Semester (All Streams)

• Research/Development Project (ED5005) The project which you have been working on throughout the summer of Year 1 and all of Year 2 is due in this semester. There are generally two options for submission: one at the start of the summer and one at the end of the summer, thus allowing you to finalise your project and dissertation during the summer of Year 2.

Books and journal articles needed for the course will be available online through the UL Glucksman Library.

For more information on each module, you can search the faculty, school and module code on UL’s Book of Modules

• Cat 1: Applicants should hold a bachelor’s degree (NFQ Level 8) with at least a second-class honour, grade 2 (2:2) in a relevant engineering, computing, mathematics, science or technology discipline

• Cat 2: You may also hold a bachelor’s degree (NFQ Level 8) with at least a second-class honour, grade 2 (2:2) in a discipline with asignificant mathematics and computing element.

• Cat 3: You may also hold a bachelor’s degree (NFQ Level 8) with at least a second-class honour, grade 2 (2:2) in a non-numerate discipline and have a minimum of three years experiential learning in computing.

• Cat 4: You may also be considered if you have at least seven years' work experience in a relevant computing or engineering environment and, or are in a senior or supervisory role in a company engaged in activities relevant to the subject matter of the programme.

• Important: You also must achieve a second-class honour, grade 2 (2.2) in the Certificate in Artificial Intelligence. This certificate is completed in the first autumn semester.

Other Entry Considerations:

We encourage you to apply even if you don’t meet the standard entry requirements, as long as you can show that you have the knowledge, skills, and experience needed for the programme.

At UL, we value all kinds of learning and support different ways to qualify through our Recognition of Prior Learning (RPL) policy.

International students:

• For details on country-specific qualifications visit postgraduate entry requirements for international students .

Checklist of Documents:

• *Academic transcripts and certificates
  • UL graduates only need to provide their student ID.
• Copy of your birth certificate or passport
• English translation of your qualifications and transcripts
• Copy of your CV
• Personal Statement
• Other relevant documentation to support their application, such professional certificates (optional)

English Language:

• English Language Competency certificate
• For details on accepted language qualifications visit English Language Requirements

Guidelines on Completing your Application

• To make sure we can review your application quickly, please:
  • Upload all documents. Your application can’t be reviewed until we have all the documents on the checklist.
  • Title the documents you are uploading. For example, "Personal Statement", "Undergraduate Transcript", "Postgraduate Transcript", "English Language Certificate" etc.
• *If you are waiting to graduate, submit your application with the documents you have to date, you don’t need to have finished final exams before applying.

EU - 4,950ドル per annum *

Non- EU - 7,350ドル per annum *

* Year 2 fees are subject to change

Please note that international study visas are only available to students studying full-time in Ireland. This programme does not qualify for a study visa.

Annual fees are billed by semester. Once registered, students may be eligible to apply for a monthly payment plan.

Further information on fees and payment of fees is available from the Student Fees Office website. All fee related queries should be directed to the Student Fees Office (Phone: +353 61 213 007 or email student.fees.office@ul.ie ).

Funding

Technology Ireland ICT Skillnet Subsidised Funding

You may be able to avail of subsidised fees from Technology Ireland ICT Skillnet. These are granted on a first-come first-served basis, are limited and subject to eligibility.

For more information and to apply for the grant-aided fees, please visit the course page on Technology Ireland ICT Skillnet .

Find further information on funding and scholarships .

Gerry Carty, Senior Engineering Manager, Avaya

"Unquestionably I would recommend the programme. The content is really strong, covers a lot of areas of AI, and the lecturers are really helpful and flexible to the student’s needs."

Michaela Dillon, Senior QA Engineer, ORBCOMM

"Overall, the course has given me an in-depth understanding of the inner workings and the pros and cons of AI. Plus, the added advantage to pursue AI opportunities within other roles and industries."

Seamus Brady, Lead AI/ML Engineer, Optum

"I would absolutely recommend the programme. I’ve got cutting edge, machine learning education, and a valuable industry-led qualification that has made changes to my career."

Still Curious?

The team regularly host and take part in webinars to support future students. If you would like to learn more or ask questions at an online information session, click below.

Online Information Sessions