Course Details

Course Code(s):
PDAICVTPAD
Available:
Part-Time
Intake:
Autumn/Fall
Course Start Date:
September
Duration:
1 Year
Award:
Professional Diploma
Qualification:
NFQ Level 9 Minor Award
Faculty: Science and Engineering
Course Type: Taught, Professional/Flexible
Fees: For Information on Fees, see section below.

Contact(s):

Name: Dr Tony Scanlan
Email: tony.scanlan@ul.ie

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Brief Description

Next Intake: September 2024 

This course qualifies for 80% funding under the HCI Fees Subsidy. Check fees section for details and eligibility.

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 detection3D 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: If you are using this Professional Diploma as the final 30 ECTS Credits to stack into our Master of Professional Practice (MPP) you will need to apply via the MPP page HERE.

Applicants eligible for ICT Skillnet funding must apply in the Summer for the Sept start. The deadline to apply for funding is 16th August 2024.

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.

Who is this course for:

You could be a graduate of a numerate discipline including computer science, engineering or physics, who wants to specialise in computer vision. This programme is also suitable for technical professionals who want to upskill in the application of deep learning to computer vision tasks. This programme requires a graduate level of ability to code in a high level language such as python, familiarity with linear algebra and basic machine learning is also recommended.

Programme Structure:

download the programme brochure HERE


 

 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.

FREQUENTLY ASKED QUESTIONS

Who is this Professional Diploma in AI for Computer Vision for? 
A programme in AI for computer vision would be beneficial for anyone interested in developing applications or systems that require image or video analysis. This is a rapidly growing field with a high demand for skilled professionals. 

Why should you/your employee sign up?
Computer vision has applications in various industries, such as healthcare, retail, manufacturing, and autonomous vehicles. By taking this programme, you/your employee can learn how to apply computer vision techniques to solve real-world problems in your organisation, such as:

  1. Automation - With computer vision skills, you can automate many repetitive and time-consuming tasks, such as quality control, inspection, and data analysis. This can save a significant amount of time and money while improving accuracy and efficiency.
     
  2. Improved customer experience - Computer vision will help your business to better understand and respond to your customers' needs. For example, computer vision can be used to analyse customer behaviour in your retail store, providing insights that can be used to optimise your store's layout or improve product placement.
     
  3. Competitive advantage - Companies that can leverage computer vision technology can gain a competitive advantage over those that don't. These skills will empower you to make better business decisions and improve the bottom line by automating tasks and gaining deep and meaningful insights.
     
  4. Innovation - Computer vision technology is constantly evolving, and businesses that invest in this area are well-positioned to stay ahead of the curve. By staying up to date with the latest developments in computer vision, your business can identify new opportunities for growth and innovation.

Can you give some more industry examples of AI for Computer Vision in use?

  • In retail, computer vision can track inventory and analyse customer behaviour. For example, AI algorithms can analyse video footage to track customer movement patterns within a store, identify products that are frequently picked up but not purchased, and even predict when inventory is running low and needs to be restocked.
     
  • In healthcare, AI with computer vision skills analyses medical images such as X-rays and CT scans to help doctors diagnose and treat patients more quickly and accurately. For example, computer vision algorithms can analyse X-rays to detect early signs of lung cancer or analyse MRI scans to identify areas of the brain that are affected by neurological conditions.
     
  • Another area where AI with computer vision skills is making a difference is autonomous vehicles. Self-driving cars rely on computer vision algorithms to analyse the road ahead, detect obstacles, and decide how to navigate safely. This technology has the potential to revolutionise transportation and make our roads safer.

As AI with computer vision skills advances, we expect more industries to adopt this technology to improve efficiency, reduce costs, and enhance overall performance. 

Can you give an example of an assignment?
Perform Classification of a Histopathology Dataset using transfer learning. - Applying transfer learning to biomedical data is difficult as the target domain is very different to the source ImageNet domain used to train the Neural Networks often used as a feature extractor for biomedical data. In this assignment, you will create a base neural network model with appropriate classifier layers for the biomedical data and fine-tune the model to maximise performance on the biomedical dataset.

How many hours per week? And what is the split between lecture and tutorial time? 
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.

What companies currently use AI for Computer Vision?
Many companies use AI for computer vision as it is a rapidly growing field with numerous applications across various industries. Some examples where AI for Computer Vision is prevalent include but are not limited to Adobe, AMCS, Amazon, Analog Devices, Apple, Cainthuis, Collins Aerospace, Dataminr, Emdalo Technologies, Ericsson, Facebook, Google, Huawei, IBM, Intel, Jaguar Land Rover, Johnson & Johnson, Microsoft, NVIDIA, Qualcomm, Shutterstock, Tesla, Twitter, Valeo Vision Systems, Xperi, GM.

What software/ programmes will I be using?

Python packages such as Numpy, sklearn, OpenCV, and Tensorflow with access to this software via Google Colab. Graduate level proficiency with a high level programming language such as python (matlab or R) is recommended for this programme.


Do I need access to anything? For example, data, software?
Access to a PC and a good internet connection suitable for live video lectures. 

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.

When does this programme start? 
This programme starts in September 2023 and finishes in May 2024. It is part-time and delivered online. 

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.

    Apply:

    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

    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

    AND

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

    HCI Fees Subsidy - Candidates who satisfy the eligibility criteria can qualify for 80% funding subject to the availability of places. To clarify eligibility please go to Eligibility Criteria 

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

     

    Please click here for information on funding and scholarships.