Statistical Machine Learning

Statistical Machine Learning encompasses a collection of techniques for discovering patterns in data and making predictions, involving models and methods at the intersection of Machine Learning and Statistics. This module provides an overview of a variety of statistical learning methods for unsupervised and supervised learning. Focus will be placed on the understanding, the critical evaluation and the appropriate application of the different techniques in different data analysis scenarios.

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. Required to have completed mathematics and statistics modules that contain elements of linear algebra (specifically matrix manipulation) and calculus.

On completion of this module, students should have acquired the following skills:

  • Have an understanding of the theory regarding all the statistical learning methods introduced
  • Being able to use the different techniques according to the context and the purpose of analysis
  • Being able to evaluate the performance of the statistical learning methods introduced
  • Use the statistical software R to implement these methods and being able to interpret the relevant output

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.

Yes, if you complete this module it can be credited as part of the MSc Data Analytics or Professional Diploma Data Analytics PT or MSc in Digital Agriculture Full Time or MSc in Digital Agriculture Part Time

Detailed Course Information

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