Course Details

Course Code:
1 Year
Professional Diploma
NFQ Level 9 Minor Award
Faculty: Science and Engineering
Fees: For Information on Fees, see section below.
Application Deadline:


Name: Ciaran Eising
Address: ECE Department
University of Limerick
Email: Telephone: 061-202577
Name: Tony Scanlan
Address: ECE Department
University of Limerick

Express Interest

Register your interest here for more information or to be notified when applications are open.

Brief Description

Next Intake: September 2022 (online)

This part-time, online Professional Diploma in AI for Computer Vision introduces students to the principles of computer vision, and rapidly advances their knowledge in state of the art vision systems. Students will learn about modern Deep Learning approaches to many machine and computer vision problems across a wide range of applications, including object detection, 3D image processing and facial recognition.

The programme is developed in collaboration with industry and focuses on peer and team learning, critical reflection and feedback incorporating coding challenges in AI and Computer Vision so that students are equipped with the skills to work confidently in the area.

Download the full course breakdown and FAQs here.

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Computer Vision is a field of Artificial Intelligence that enables a computing system to derive meaningful information from digital images, video, and related signals.

The programme introduces students to the principles of Computer Vision, rapidly advancing the students knowledge of this innovative filed of technology. 

It is suitable for those who want to upskill in computer vision, working in engineering and technical roles or graduates with a degree in numerate disciplines including Engineering, Computer science and Physics.

Course Breakdown:

Semester 1: September - December

  • Introduction to Scientific Computing for AI – CE4021
  • Machine Vision and Image Processing 

Semester 2: January - May

  • Geometric Computer Vision - CE5002
  • Deep Learning for Computer Vision

Both Semesters:

  • Future Focused Professional Portfolio 1&2:


Assessment is based on group work, regular e-tivity assessments including biweekly coding tasks and short online quizzes. Students complete two future focused professional portfolio modules, which will involve group work and development of a presentation/event on a relevant topic(s). Final grade will be associated with key project deliverables in each module, as well as engagement in the process week -to-week.

Download the full course breakdown and FAQs here.


The part-time course is delivered fully online with 15-18hrs commitment per week. A one hour weekly live session will be held 6pm and tutorials will be held at a time that suits students. All material will be recorded for self-directed learning, and students will be supported by their lecturers and tutors.

On successful completion of this programme, the graduate will be able to:

  • Utilise and design state of the art computer vision techniques and algorithms for a wide range of applications in automotive safety, manufacturing, security, and consumer devices.
  • Work in technical, engineering, and scientific roles requiring computer vision.


Applicants are normally expected to hold a primary honours degree in a cognate (related) discipline, (minimum H2.2), or equivalent and have at least 5 years of relevant industrial experience.

Alternative Entry Route:

In accordance with the University's policy on the Recognition of Prior Learning candidates who do not meet the minimum entry criteria may be considered. These candidates will be required to submit a portfolio to demonstrate their technical and/or management experience. An interview with the course admission team is also required to ensure candidates have the experience, motivation, and ability to complete and benefit from this course.

What to Include with your Application

  1. Photo or Scanned original copy of your transcripts for all years of study. (Graduates of UL need only provide us with their Student ID number.)
  2. Photo or Scanned copy of passport to verify ID and full legal name.
  3. A copy of your most recent CV.

English Language Requirements:

Applicants who do not have English as their first language may satisfy English Language requirements if your qualifications have been obtained in a country where English is an official language this will suffice

If this is not available, the following additional documents must be provided:

  • English translation of your qualification(s)/transcripts


  • English language competency certificate
Type Fee Deposit*
EU €3,500 €250
Non-EU €4,750 €600

*The deposit is required once an offer is issued to secure a place.

For information on funding and scholarships please click here.

Please click here for information on funding and scholarships.

Where is Computer Vision used today?

Companies where AI for Computer Vision is prevalent include, but are not limited to: AMCS, Amazon, Analog Devices, Cainthuis, Colins Aerospace, Dataminr, Emdalo Technologies, Ericsson, Facebook, Huawei, IBM, Intel, Jaguar Land Rover, Johnson & Johnson, Qualcomm, Shutterstock, Valeo Vision Systems, Xperi, GM, Twitter.

What software/ programmes will I be using?

Python packages including, Numpy, sklearn, OpenCV, Tensorflow with access to this software via Google Colab. However, prospective students do not need to already have experience with these tools – you will be taught them during the programme.

Do I need access to anything? Eg. Data, software?

Access to a reasonably modern PC and a good internet connection that is suitable for live video lectures. All software used in the programme is open source and freely available.

Who is leading the course?

Dr. Tony Scanlan

Dr. Scanlan is a Senior Research Fellow in the UL Dept. of Electronic & Computer Engineering. He has extensive experience in the field of microelectronics and signal processing in association with multinational and SME industrial partners. Dr. Scanlan’s current research interests are in the application of Artificial Intelligence (AI) & computer vision to manufacturing inspection, environmental monitoring and consumer & media applications.

Dr. Ciarán Eising

Dr. Eising has extensive experience working in computer vision, having worked for >15 years designing computer vision algorithms and systems for driver assistance and automated driving solutions with Valeo Vision Systems. His research in UL focuses on the applications of artificial intelligence and computer vision in areas such as medical imaging, waste management and automated driving.

Dr. Pepijn van de Ven

Dr. van de Ven is a Senior Lecturer in Artificial Intelligence and Machine Learning and a Course Director for UL’s national online MSc in AI, an industry driven fast-paced masters to upskill Ireland’s workforce in use of Artificial Intelligence.