| Module code | MIT 801 |
| Qualification | Postgraduate |
| Faculty | Faculty of Engineering, Built Environment and Information Technology |
| Module content | In this module students will be exposed to different categories of machine and statistical learning algorithms that can be used to manipulate big data, identify trends from the data, modelling trends for prediction purposes as well as modelling for the detection of hidden knowledge. Students will be exposed to various machine and statistical learning algorithms/methods and they will learn how to make the right choice with regard to these. Learning, in a supervised and unsupervised mode will be covered. Furthermore students will develop a practical understanding of methods that can aid the learning process, such as, new developments in regression and classification, probabilistic graphical models, numerical Bayesian and Monte Carlo methods, neural networks, decision trees, deep learning and other computational methods. This module also includes a visualisation component focusing on the encoding of information, such as patterns, into visual objects. |
| Module credits | 15.00 |
| NQF Level | 09 |
| Programmes | |
| Prerequisites | First year level higher education modules in Computer Science, Mathematics and Statistics. |
| Contact time | 16 contact hours per semester |
| Language of tuition | Module is presented in English |
| Department | School of Information Technology |
| Period of presentation | Semester 1 |
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