Computer Vision and Artificial Intelligence - MEng

During this programme, you will:

• Develop knowledge in state-of-the-art vision systems, artificial intelligence algorithms, and machine learning applications.

• Learn about modern Deep Learning approaches to many machine and computer vision problems.

• Acquire Computer Vision skills that are widely used in a range of industries, including automotive, virtual reality, augmented reality, robotics, medicine, security, aerospace and consumer electronics.

• Deliver a significant digital futures project or piece or research in the area to apply and deepen your learning.

Key information:

• Complete full-time over one year

• Delivered on campus

• Modules taught during autumn and spring semesters

• Option to exit after the Spring Semester with a Postgraduate Diploma

• Submit dissertation or Engineering Project at the end of summer semester

• Option to replace summer semester to develop a digital portfolio with a Digital Futures Innovation steam

You will learn through a blend of:

• Lectures, workshops, and hands-on activities

• Reflective practice and guided research

• Regular feedback from faculty and peers

Year 1

Autumn Semester

• Introduction to Data Engineering and Machine Learning (CE4051) covers mathematical and coding skills essential to developing machine learning applications in Python and introduces more advanced machine learning topics.

• Artificial Intelligence (CE4041) provides a solid theoretical and practical understanding, knowledge and skill in the application of artificial intelligence and expert systems.

• Machine Vision (CE6021) provides a detailed insight into image formation, formats and processing necessary so computers can use machine vision technologies.

• Computer Vision Systems (CS6461) equips students with skills for the development of real-world robust computer vision systems.

• Artificial Intelligence and Governance (IN6031) provides a comprehensive understanding of the governance, ethics, and social and business impacts of AI.

Spring Semester

• Introduction to Engineering Research Methods (CE6026) supports students in finding a suitable research topic, developing a detailed specification, and preparing for the academic writing of their report.

• Theory and Practice of Advanced AI Ecosystems (CS5024) prepares students to use new technologies for the sharing, composition, reuse, and retrieval of algorithms and data relevant to the production and use of Artificial Intelligence.

• Deep Learning at the Edge (EE6008) develops insight into deep learning and associated frameworks relevant to edge computing.

• Geometric Computer Vision (CE5002) provides students with the means to use cameras to reconstruct the structure and shape of the environment in which the camera is located.

• Deep Learning for Computer Vision (CE5021) equips students with a full understanding of how to design and build deep neural networks for their own applications.

Summer Semester

Option 1: Master of Engineering Project – Computer Vision and AI

• Master of Engineering Project – Computer Vision and AI (CE6025) facilitates students to undertake and complete a significant project, involving an advanced design and implementation task related to Computer Vision and Artificial Intelligence.

Option 2: Digital Futures and Innovation Stream

A number of students may choose to follow this stream, developing a professional portfolio as an alternative to the traditional project thesis.

• Digital Futures Lab (MI6103) enables students to address complex organisational and societal problems through mapping, analysis and creative thinking.

• Master of Engineering Digital Futures Project (CE6013) facilitates students to complete a technical prototype based on their portfolio work in the Digital Futures Lab.

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

• Applicants should hold a bachelor’s degree (NFQ Level 8) with at least a second-class honour, grade 2 (2:2) in a relevant discipline like engineering, computing, mathematics, science or technology discipline, or another discipline where significant math and computing elements can be demonstrated.

• The university may shortlist and invite you to an interview.

Linear Algebra is a key mathematical requirement for this programme. If you can answer the questions in the Linear Algebra Self-Assessment Worksheet, you will be well equipped for the course.

Admission to the programme is a competitive process, and unfortunately not all applicants that meet the criteria will be offered a place.

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

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 - 7,900ドル

Non- EU - 19,700ドル

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

Find further information on funding and scholarships .

This course can lead to the following sectors and careers:

• Computer Vision/ Machine Learning Engineer
• Data Scientist
• Product Manager
• Researcher

Computer Vision is widely used in:

• Manufacturing
• Social media
• Automotive
• Virtual reality
• Augmented reality
• Robotics, medicine
• Security
• Aerospace
• Consumer electronics
• Agricultural Technology

Micheal Cassidy, Chief Technical Officer, Irish Manufacturing Research (IMR)

"At IMR, we actively seek engineers and researchers possessing the expertise nurtured through specialised courses like the MEng Computer Vision and Artificial Intelligence offered at the University of Limerick.

In order to continue to support and improve the competitiveness of Irish manufacturing companies, we remain committed to fostering a collaborative relationship with the University of Limerick to nurture and harness the talent emerging from this program. Ultimately the skills and talent arising from this course will support IMRs vision of de-risking, demystifying and delivering impactful research."

Dr. Eamon Hynes, Chief Technical Officer, AMCS Group

"We have invested heavily in computer vision and AI within our products to drive value for our customers using the latest emerging technologies. At AMCS, we are actively seeking experienced and graduate engineering candidates in AI, computer vision and software engineering, such as graduates from the MEng in Computer Vision and AI at the University of Limerick to enable us to continue to drive technological advances to our customers and their end users."

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