This unit introduces the problem of machine learning and the major kinds of statistical learning used in data analysis. Learning and the different kinds of learning will be covered and their usage discussed. Evaluation techniques and typical application contexts will presented. A series of different models and algorithms will be presented in an exploratory way: looking at typical data, the basic models and algorithms and their use: linear and logistic regression, support vector machines, Bayesian networks, decision trees, random forests, k-means and clustering, neural-networks, deep learning, and others. Finally, two specialist topics will be covered briefly, statistical learning theory and working with big data.
Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled teaching activities.
Compare and contrast the differences between big data applications and regular applications of algorithms;
Describe and apply the major models and algorithms for statistical learning;
Describe the theoretical limits of learning.
Evaluate a machine learning algorithm in typical contexts;
Differentiate kinds of statistical learning models and algorithms;
Describe what machine learning is;
Identify the most competitive algorithms for typical contexts;
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