Computer vision applications have significantly expanded over the last decade and this core skill set is always in high demand by employers. This module 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 module 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 and 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.
Taken on its own the module provides key skills that can immediately be applied in the IoT sector. It can also be taken as a sample of what is on offer for the MEng in Electronic and Computer Engineering and credits obtained in this module can be applied to the programmes at a later date.
For more information about this course, please contact Conor McArdle.
For all module offerings on Single Module Programmes, applicants should have an honours primary degree (Level 8 NFQ) or equivalent in a cognate area. In the case of all applicants to Dublin City University evidence must be provided of competence in the English language. The English Language requirements of DCU can be found here.
Please visit www.pac.ie to make an application, using the following programme code:
DC897 SMPEC, Faculty of Engineering & Computing
You will then be able to select your module choice from the drop down menu.
Please visit www.pac.ie to make an application:
Applicants must submit;
- Certified academic transcripts for each and every year of study
- Evidence of English Language *
- Copy of valid Passport
- A passport size photograph
- Confirmation of nationality and residence (see below **)
- Confirmation of Fee eligibility category (see below ***)
*Where candidates are non-native speakers of the English language, they must satisfy the university of their competency in the English language (http://www.dcu.ie/registry/english.shtml ).
** Important Eligibility Documentation
Eligible Applicants must be ordinarily resident in Ireland and must meet the nationality and EU residency rules as aligned to Springboard as detailed here https://springboardcourses.ie/faq Applicants must provide supporting documentation to evidence that they meet this criteria at the time of application. Failure to provide adequate supporting documentation may lead to your application not being assessed in time for consideration for a place on these job stimulus programmes.
*** Applicants must self-declare their fee eligibility category and provide all supporting documentation to evidence they meet the criteria at the time of application. Failure to provide adequate supporting documentation may lead to your application not being assessed in time for consideration for a place on these job stimulus programmes. For further information on eligibility categories and the impact on fee liability, please visit https://springboardcourses.ie/faq.
Applications will be accepted on a rolling basis until the programme is full or until 1st December 2020. Please note all supporting documentation must be submitted by this date. Only completed applications will be considered.
Admission queries can be directed to firstname.lastname@example.org