Machine Learning

Learn about the standard shallow forms of machine learning, Deep Learning and Convolutional Neural Networks for use in computer vision tasks. The module will look at training strategies and frameworks for Deep Learning as well as reflect on the ethical implications of machine learning.

Accordion / FAQ component

Most modules require a 2.2 degree in a related discipline or equivalent professional experience. Should you have any queries regarding your eligibility, please contact us at info@advancecentre.ie

N.B. Students should have a primary degree and have covered Linear Algebra, Statistics and Probability

On completion of this module the learner will/should be able to:

  • Compare engineered detectors with machine learning techniques in terms of performance on appropriate metrics and data sets and determine the appropriateness of each for safety critical applications.
  • Apply transfer learning to adapt a pre-trained network to a new classification problem.
  • Assess the validity of various cost functions to specific machine learning problems.
  • Effectively collaborate and communicate with others in the timely development of solutions to machine learning problems, including reports and software.
  • Design, test and evaluate deep network architectures.
  • Appreciate the data rights of citizens and the constraints these apply to the use of pattern detection in real world scenarios.

Learn from world renowned academic staff in Ireland’s leading, future focused and globally recognised colleges.

Gain an accredited NFQ qualification/micro credential that you may count towards a full award if you so wish in the future.

Previous modules may be used as recognition of prior learning towards Advance Centre degree programmes.

Equip yourself with the latest in demand skillset, tools, know-how and knowledge to succeed in your career.

Gain a competitive edge, influence growth and steer strategic goals in your organisation upon completion of your studies with the Advance Centre.

Detailed Course Information

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