This programme is suitable for those who wish to upskill in computer vision for improved accuracy and efficiency within their workplace. Also, it offers widened opportunities for career advancement. It is of great benefit to a wide range of working professionals, including:
Software Developers: AI for Computer Vision is an emerging field that requires software developers to be familiar with algorithms and computer vision techniques. This programme will help you to enhance your skills and stay up-to-date with the latest developments in this field.
Data Scientists: Data scientists collect, analyse, and interpret large amounts of data. This programme will enable you to extract valuable insights from visual data.
Engineers: Engineers, especially those in robotics, autonomous vehicles, drones, manufacturing, public safety and social media, will benefit from understanding computer vision techniques. This new knowledge and skills will help you develop more sophisticated and advanced systems.
Product Managers: Product managers are responsible for managing the development of products and services. Understanding AI for computer vision will empower you to develop new products and services that leverage visual data.
Researchers: Computer science, artificial intelligence, and robotics researchers will benefit from understanding the latest developments in AI for computer vision. This will help you develop new algorithms and techniques to advance the field further.
Overall, this AI for Computer Vision programme benefits those in technical roles which deal with visual data. Graduates with a degree in numerate disciplines, including Engineering, Computer science and Physics, would also benefit from this programme.
This part-time programme is delivered online with a 15 to 18hr commitment per week. A one-hour weekly live session will be held at 6pm on a nominated weeknight, and tutorials will be held at a time that suits students. All material will be recorded for self-directed learning; however, you will be supported by your lecturers and tutors.
Who is leading this programme?
Dr Tony Scanlan
Dr Scanlan is a Senior Research Fellow in the UL Dept. of Electronic & Computer Engineering. He has extensive experience in microelectronics and signal processing in association with multinational and SME industrial partners. Dr Scanlan’s current research interests are applying 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 artificial intelligence and computer vision applications 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 using Artificial Intelligence.
Funding Eligibility
Applicants must be working in a private or commercial semi-state organisation registered in the Republic of Ireland to avail of the grant-aided fees. As a government-funded training network, we can only support those meeting these criteria.
Applicants who do not meet our funding criteria may in some cases be able to apply directly to the college and pay the full fee of €3,500 if there are places available. Contact us for more information.
Academic Eligibility
Applicants are normally expected to hold a primary honours degree in a related discipline, (minimum H2.2).
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.
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
AND
- English language competency certificate
Fees
Full Course fee per annum: €3,500
Skillnet grant per annum: €700
Student fee per annum: €2,800*
*Part-funded fees are only available to eligible applicants.
Click “Apply now” and attach your updated CV to be assessed for funding eligibility.
Alternatively, email info@ictskillnet.ie with the below information:
– Subject line “Professional Diploma in Artificial Intelligence for Computer Vision”
– Current Employer
– Current Highest NFQ
– Attach your CV
The University of Limerick has sole discretion and is the final arbiter on who will participate. Making an application is not a guarantee of selection. Applications are treated in strictest confidence.
Semester 1
Introduction to Scientific Computing for AI
You will begin by taking a range of Artificial Intelligence-related modules and learning about associated scientific computing, programming language and host platforms. You will explore Python, numerical computing with Numpy, Linear Algebra, randomness and probability, classifiers and optimisation.
Machine Vision & Image Processing
This module will focus on 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 meaningful engagement with this material. This module is a precursor to advanced vision modules, which requires a good understanding of these key principles.
Semester 2
Geometric Computer Vision
Geometry describes the structure and shape of the environment in which a camera is located. You will learn about the process of determining the structure of the environment, the position and orientation of the camera, and how the camera moves in relation to the environment through the analysis of camera image streams. This subfield of computer vision is commonly used in mobile robotics, vehicle autonomy and augmented reality.
Deep Learning for Computer Vision
Deep learning has become the dominant approach to designing solutions for everyday computer vision tasks. In this module, we will examine the application of deep learning to the key computer vision tasks of image classification, object detection and semantic segmentation. We will also discuss fundamental concepts in the design and structure of deep neural networks. You will gain a complete understanding of how to design and build networks for your workplace applications.
Future Focused Professional Portfolio 1 & 2
In the first module, you will be led through a series of talks about the future of technology, the future of markets, and the future of society as a whole. You’ll work collaboratively to identify key trends impacting your role and organisation. You’ll also build a professional network and use it to reach out to key thought leaders in this area.
The second module will provide you with an opportunity to demonstrate independent and self-determined learning through the creation of your own individual portfolio. Your portfolio includes various activities that will show how you’ve improved your reflective practice, how well you’ve used discipline-specific knowledge in different situations, and how you’ve led a discussion about the future of your field.
How are learners assessed on this programme?
There are no terminal exams on this programme. Assessment will be continuous; you will be asked to prepare a media plan that will be developed for your chosen company.
