Computer Vision

Computer vision applications have significantly expanded over the last decade and this core skill set is always in high demand by employers. This microcredential will build on the basic concepts with a view to delving deeper into core computer vision, machine learning and deep learning topics. As well as examining traditional computer vision concepts (i.e. feature extraction and machine learning) a key focus of the microcredential will be on deep learning as applied to computer vision. We will examine the core concepts behind deep learning for computer vision with a specific focus on Convolutional Neural Networks (CNN). Students will learn how to design and tune such networks in a range of practical applications and assignments. In addition we will examine a range of deep learning architectures ranging from AlexNet up to the current state-of-the-art in this ever expanding field. Deep learning based computer vision forms the core of many of the recent developments in this field and has been widely adopted as a core AI tool by all the key industrial players such as Google, Facebook, IBM, Apple, Baidu as well as a wide range of highly innovative startups. All computer vision and deep learning concepts will be reinforced by guided practical work and case studies. This microcredential is primarily aimed at those who aim to undertake research in computer vision or require a deeper understanding of the subject to address commercial computer vision development. Computer vision applications span a wide range of disciplines including industrial/machine vision, video data processing, biomedical engineering, healthcare, astronomy, imaging science, sensor technology, multimedia and enhanced reality systems.

1. Recall, review and analyse the advanced theories, algorithms, methodologies and techniques involved in traditional and deep learning based computer vision.

2. Illustrate their ability to comprehend and interpret issues relating to the design of advanced traditional and deep learning based computer vision.

3. Synthesise and evaluate the relevant merits of competing advanced computer vision techniques.

4. Apply computer vision techniques in a range of application scenarios.

5. Develop a deep understanding of the issues involved in evaluating computer vision system implementation.

6. Demonstrate the ability to implement a computer vision pipeline.

A Primary Honours degree, Level 8 in Electronic/Electrical/Computer Engineering, Applied Physics, Computer Sciences or other Cognate/Engineering Disciplines. Applications are also invited from diverse educational and/or employment backgrounds, with applications evaluated on a case-by-case basis. 

And also to indicate the required documentation:

• Please provide Academic Transcripts for final year of study where appropriate (English translation)
• All applicants must submit a copy of their passport
• Please upload a CV

If applicable, evidence of competence in the English language as per DCU entry requirements.  Please see here.