Available: Full- Time
Duration: 1 Semester
Award: Specialist Diploma
Qualification: NFQ Level 9 Minor Award
Faculty: Science and Engineering
Course Type: Taught Professional/Flexible
Fees: For Information on Fees, see section below.
Application Deadline: Sunday, January 10, 2021
The application deadline for these programmes has been extended to January 10th 2021 to accommodate demand. Please note programmes may close in advance of deadline.
Artificial Intelligence (AI) is one of the most exciting areas in computer science and engineering.AI deals with creating machines having the capability to learn adapt and exhibit intelligence. AI research is revolutionising our lives and leading us to a world with self-driving cars, automated trading on stock markets, AI-assisted surgery, AI-controlled power grids, smartphones that can recognize objects / faces / speech, search engines that can translate languages, video games that exhibit responsive, adaptive and intelligent behaviour.
This specialist diploma embraces the new trends in AI as well as combining established AI techniques, such as: Unsupervised/Supervised/Reinforcement learning, Deep Learning, Parametric/Nonparametric regression and classification, with emerging disciplines and technologies: Deep Reinforcement Learning, Automatic compliance checking, Automatic workflow synthesis, Machine-supported correctness and Explainable AI.
The delivery of the programme is through traditional classroom learning, problem-solving method and blended learning, and self-directed study, and focuses on the undertaking of a set of AI related modules.
It will be delivered primarily via lectures, supported with seminars, tutorials, labs, assignments and projects. Formative assessment will be based on assignments and project work with a focus on acquiring knowledge, skills, and ability to develop ad-hoc algorithms, with a focus on creativity, and autonomous learning.
Summative assessment will typically be exam based.
CS6462 PROBABILISTIC AND EXPLAINABLE AI (6 ECTS)
CS6482 DEEP REINFORCEMENT LEARNING( 6 ECTS)
CS5062 DATA ANALYTICS (6 ECTS)
CS5024 THEORY AND PRACTICE OF ADVANCED AI ECOSYSTEMS (6 ECTS)
CE6012 ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING (6 ECTS)
At the end of this programme students will be able to:
- Demonstrate an understanding of/Implement Artificial Intelligence and Machine Learning
- Use Artificial Intelligence and Machine Learning to solve problems.
- Develop ad-hoc algorithms that go beyond traditional methods.
- Implement and critique techniques for image processing, natural language processing and data analytics.
- Design and Implement solutions to a range of AI and machine learning
- Use appropriate technologies frameworks and platforms for the efficient execution of AI
- Explore and use AI and machine learning technologies, demonstrating an understanding their core features and usability.
- Demonstrate an understanding of the fundamental tools required for AI systems.
- Achieve an informed understanding of general AI frameworks, such as deep reinforcement
learning and being able to apply it to real-problems and simulated environments.
- Identify, develop and implement appropriate existing and emerging AI and machine learning solutions for practical problems.
- Acquire new knowledge and skills with confidence in emerging AI areas.
- Use objective approaches to compare and select the techniques and algorithms to solve specific problems.
- Build confidence and develop insight into some of the fundamentals and developments in the field of AI.
- Gain awareness of the limitations of traditional machine learning systems and demonstrate an understanding of the benefits of explainable AI.
- Demonstrate an understanding of the criteria and the approaches behind the requirements for and the delivery of assurance and quality in complex AI ecosystems.
This programme has secured Government funding through the July Stimulus Postgraduate Skills package. Eligible applicants can avail of 90-100% funding. Please click here for eligibility criteria
What to include with your application
- Qualification transcripts and/or certificates (including certified English translations if applicable)
- English language qualifications (if English is not your first language)
- A copy of your most recent CV
- A copy of your passport or birth certificate (long version)
- Proof of status - Please click here for more information
Academic Entry Requirements
Applicants for must have a first or second class Level 8 honours degree (NFQ or other
internationally recognised equivalents) in a relevant or appropriate subject (Computer Science, Computer Engineering programmes, or level 8 Science programmes that have sufficient mathematical and programming skills), or equivalent prior learning that is recognised by the University as meeting this requirement.
Applicants must also satisfy the English Language Requirements of the University. The University reserves the right to shortlist and interview applicants as deemed necessary.