Course Code: MSAIMLTFAD
Duration: I Year FT
Faculty: Science and Engineering
Course Type: Taught
Fees: For Information on Fees, see section below.
Artificial Intelligence (AI) is one of the most exciting areas in computer science and engineering. AI and Machine Learning (ML) address the challenge of 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 behavior.
This new unique Programme embraces the new trends in AI by combining established AI techniques like:
- Unsupervised/Supervised/Reinforcement learning, Deep Learning, Parametric/Nonparametric regression and classification, Evolutionary Computation, Bayesian Networks and Search Algorithms,
with emerging disciplines and technologies such as:
- Deep Reinforcement Learning, Causality modelling, Bioinspired robotics, Evolutionary robotics, Humanoid robotics, Games-inspired Behavior modelling, Automatic compliance checking, Automatic workflow synthesis, Machine-supported correctness, Probabilistic programming, and Explainable AI.
Emphasis is on applying these techniques to a variety of application areas: text analytics and natural language processing, machine vision, games, smart manufacturing, predictive maintenance, motion and planning, business analytics, finance and many others.
|Spring Semester||Summer Semester|
|Introduction to Data Engineering and Machine Learning||Artificial Intelligence and Machine Learning||Artificial Intelligence and Machine Learning Project|
|Evolutionary Computation and Humanoid Robotics||Deep Reinforcement Learning|
|Text Analytics and Natural Language Processing||Probabilistic and Explainable AI|
|Artificial Intelligence for Games||Theory and Practice of Advanced AI Ecosystems|
|Machine Vision||Research Methods and Project Specification|
Content of modules can be found by using the search option on the book of modules.
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.
WHAT TO INCLUDE WITH YOUR APPLICATION:
- Qualification transcripts and/or certificates (including certified English translations if applicable
- English language qualification(s) (if English is not your first language)
- A copy of your birth certificate/passport
English Language Requirements
Applicants whose first language is not English must provide evidence of either prior successful completion of a degree qualification taught through the medium of English or meet one of the criteria below (no longer than two years prior to application):
Acceptable English Language qualifications include the following:
- Matriculation examinations from European countries where English is presented as a subject and an acceptable level is achieved
- Irish Leaving Certificate English –Ordinary Level Grade D or above
- TOEFL – 580 (paper based) 90 (internet based)
- IELTS – Minimum score of 6.5 with no less than 6 in any one component.
- English Test for English and Academic Purposes (ETAPP) – Grade C1
- GCE ‘O’ level English Language/GCSE English Language – Grade C or above
- University of Cambridge ESOL –Certificate of Proficiency in English - Grade C / Certificate in Advanced English Grade A
- GCE Examination Boards – Oxford Delegacy of Local Examinations – Grade C / Cambridge Local Examinations Syndicate – School Certificate Pass 1-6 / University of London Entrance and School Examinations Council – School Certificate Pass 1-6
Results in examinations other than those listed above may also be accepted as meeting our English language requirements. Contact the International Education Division for advice.
This course prepares students for industry careers in data analytics, data science, machine learning and AI. It also aims to give the students the theorethical knowledge to potentially become high profile PhD candidates and who can carry out high profile research in AI.