STA5068Z - Machine Learning

15 credits at NQF level 9

Entry Requirements:

Acceptance into Master’s programs in Advanced Analytics, Data Science or Biostatistics, and/ or statistical and computing background deemed sufficient by the Head of Department.

Course Outline:

This course serves as an overview of the increasingly important field of Machine Learning. Topics covered include the fundamentals of the Machine Learning Paradigm, the Vapnik-Chervonenkis Inequality, the Bias-Variance Tradeoff, Regularization, Cross-Validation, Linear and Nonlinear Dimension Reduction, Support Vector Machines, Neural Networks, Convolutional Neural Networks, and other contemporary topics in Machine Learning. The course may not be offered every year.