Artificial Intelligence for Computer Vision

Artificial intelligence (AI) with computer vision skills has seen a business application surge in recent years. Computer vision refers to the ability of computers to interpret and understand the visual world, including images and videos. AI with computer vision skills combines this ability with machine learning algorithms to create intelligent systems that can analyse, process, and interpret vast amounts of visual data in real time. Taking an example of use from the manufacturing industry, computer vision algorithms can analyse images of products on an assembly line to identify defects and other issues that would be difficult for humans to detect. This can help improve product quality and reduce waste, leading to cost savings for manufacturers.

We offer a part-time, online Professional Diploma in AI for Computer Vision that will introduce you to the principles of computer vision, develop your knowledge of cutting-edge vision systems, and empower you to apply these skills to your workplace. You will learn about modern Deep Learning approaches to many machine and computer vision problems across a wide range of industry-focused applications, including object detection, 3D image processing and facial recognition.

This innovative Level 9 programme is co-designed with industry. It focuses on peer-to-peer and team learning, critical reflection and feedback incorporating coding challenges in AI and Computer Vision to give you the skills to work confidently in this in-demand area.

Note* This programme is available at both an introductory and advanced level. Information on each pathway can be found under the Programme Content tab below.

UL@Work programmes are co-designed with industry to ensure skills transfers are industry-responsive for current and future needs. Our programmes are developed for working professionals and are designed to be flexible and accessible. You can take it online and from anywhere worldwide.

Stack your learning with Microcreds:
Not quite ready to commit to a full professional diploma? Consider trying a microcredential. You can stack your microcred credits towards a professional diploma at a later date. Modules listed within the Programme Content with an (M) beside them are MicroCreds and can be taken independently.

Build Your Master Plan: Stack Your Way to Success: Interested in earning a master's degree beyond a Professional Diploma? Our NEW innovative and one-of-a-kind Master of Professional Practice, combines three Professional Diplomas over five years. Go to for more information https://www.ul.ie/gps/courses/professional-practice-masters

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.

We are delighted to offer this Level 9 Professional Diploma at both introductory and advanced levels so that you can engage with the programme at a suitable level. The introductory programme, Scientific Computing & Computer Vision pathway, will arm you with introductory knowledge if you are new to computer vision and artificial intelligence (AI) and without experience in Python. The advanced programme, Artificial Intelligence & Computer Vision pathway, challenges those with prior experience in Python to deepen their knowledge and skills. This approach allows you to access the programme at a level that works for you to make the most of your learning experience. We would recommend the advanced pathway to those who have previously completed a MSc in AI programme or have good knowledge of machine learning and python principles.

See the module content for the introductory and advanced options below. When you register for this programme, you will be prompted to choose the pathway you wish to enrol in.

INTRODUCTORY - SCIENTIFIC COMPUTING & COMPUTER VISION

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 (M)
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.

Both Semesters
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.

ADVANCED - ARTIFICIAL INTELLIGENCE & COMPUTER VISION

Semester 1
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.

Machine Vision & Image Processing (M)
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.

Intelligent Visual Computing and Applications
This module focuses on deep learning applications to critical computer vision applications, including facial recognition and 3D reconstruction. The use of transformer networks to build state-of-the-art computer vision systems is also discussed.

Both Semesters
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.

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.

What to Include with your Application

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.)

Photo or Scanned copy of passport to verify ID and full legal name.

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
AND
English language competency certificate

Application Deadline: Thursday, August 17, 2023

1 year part-time