Department: BSc in Computer Science
Module Description: Both the study of Artificial Intelligence - understanding how to build learning machines - and the business of developing tools to analyse the numerous increasing data sources involves developing a systematic understanding of how we can learn from data. A principled approach to this problem is critical given the wide differences in the places these methods need to be used.
This course is a foundational course for anyone pursuing machine learning, or interested in the intelligent utilisation of machine learning methods. The primary aim of the course is enable the student to think coherently and confidently about machine learning problems, and present the student with a set of practical tools that can be applied to solve real-world problems in machine learning, coupled with an appropriate, principled approach to formulating a solution.
This course avoids the potential pitfalls of simply presenting a set of machine learning tools as if they were an end in themselves, but follows the basic principles of machine learning methods in showing how the different tools are developed, how they are related, how they should be deployed, and how they are used in practice. The course presents a number of methods in machine learning that are increasingly used, including Bayesian methods, and Gaussian processes.
This course is identical to the level 10 version except for an additional learning outcome, and a consequential difference in assessment.
Braga-Neto Ulisses de Mendonc̀§a (2020). Fundamentals of pattern recognition and machine learning. Cham, Switzerland: Springer. Request PDF
Murphy, K. P. (2012). Machine learning: a probabilistic perspective. Cambridge, Mass.: MIT Press. Purchase eBook
Mitchell, T. M. (1997). Machine learning. New York: McGraw-Hill.
