MSc in Artificial Intelligence and Machine Learning

Key Points

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Artificial Intelligence (AI) is one of the most exciting areas of computer science and engineering. AI and machine learning (ML) address the challenge of creating machines that have the ability to learn, adapt, and exhibit intelligence. AI research is revolutionizing our lives and is leading us to a world with autonomous vehicles, automated trading in 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 unique new program embraces new trends in AI by combining established AI techniques such as:

  • Unsupervised / supervised / reinforced learning
  • Deep learning
  • Parametric / non-parametric regression and classification
  • Evolutionary Computing
  • Bayesian networks and search algorithms

    With emerging disciplines and technologies such as:
  • Deep reinforcement learning
  • Causality modeling
  • Bioinspired Robotics
  • Evolutionary Robotics
  • Humanoid Robotics
  • Game-inspired behavior modeling
  • Automatic compliance check
  • Automatic workflow synthesis
  • Machine-assisted correction
  • Probabilistic programming and explainable artificial intelligence.

The emphasis is on applying these techniques to a variety of application areas: text analytics and natural language processing, computer vision, gaming, intelligent manufacturing, predictive maintenance, motion and planning, business analytics, finance, and many others.

Career options

This course prepares students for industry careers in data analytics, data science, machine learning and artificial intelligence. It also aims to provide students with the theoretical knowledge to potentially become high-profile PhD candidates and to be able to conduct high-profile research in AI.

Program structure

Semester 1 – Fall

  • Introduction to data engineering and machine learning
  • Evolutionary computing and humanoid robotics
  • Text analysis and natural language processing
  • Artificial intelligence for games
  • Machine vision 

Semester 2 – Spring

  • Artificial intelligence and machine learning 
  • Deep reinforcement learning
  • Probabilistic and explainable AI 
  • Theory and Practice of Advanced AI Ecosystems
  • Research methods and project specification

Semester 3 – Summer

  • Artificial Intelligence and Machine Learning Project

Admission requirements

  • Applicants must have a first or upper second class (NFQ or other internationally recognised equivalents) level 8 degree 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 fulfilling this requirement. 
  • In the case of international candidates, the foreign equivalent is required. In addition, a degree officially translated into English will be required.
  • IELTS 6.5, with no less than 6 in any component (or its internationally recognised equivalent).

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