Machine Learning MQF LEVEL
6
STMARTINS.EDU/COMPUTING
TOTAL CREDITS: 7ECTS
This unit follows Introduction to Intelligent Systems, providing learners with a deeper look into specific learning algorithms in the sphere of machine learning. The unit looks at different types of algorithms in various categories, including supervised, unsupervised and re-inforcement learning. It includes neural network as a branch of AI, including feed-forward NN and recurrent NN. Learners will be exposed to such algorithms and will have the ability of designing learning systems according to the type of problem at hand. At the end of this unit the student will have acquired the responsibility and autonomy to: — decide which category of machine learning is best suited to the task at hand; — decide which algorithm is best suited to the task at hand; — apply the necessary rules to formulise a given problem into the specifications of the neural network; — perform any pre-processing and post-processing needed on input dataset and the output respectively; — build a learning system to tackle the problem and tweak its parameters to fine tune its accuracy and precision. The assessment for this unit will be computed using the following weighting system: — 30% Assessed Coursework — 70% Examinations
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