For all relevant UOL programmesST3189 Machine LearningIn the last decade there has been a remarkable growth in machine learning. Following recent advances in gathering, storing and managing vast amounts of observations, the ability to process high dimensional data and deal with uncertainty becomes increasingly important. Despite the increase of available information, inference may still lead to false conclusions in the absence of a suitable methodology.
This course covers a wider range of such model based and algorithmic machine learning methods, illustrated in various real-world applications and datasets. At the same time, the theoretical foundation of the methodology is presented is some cases. PrerequisiteIf taken as part of a BSc degree, the following courses must be passed before this course may be attempted:ST104a Statistics 1 and ST104b Statistics 2 and (either MT105a Mathematics 1 with MT105b Mathematics 2 or MT1174 Calculus).
Aims and objectives To provide an in-depth introduction to supervised and unsupervised learning To present some of the main models and algorithms for regression, classification and clustering Other topics include Bayesian inference, Monte Carlo methods and dimension reductionAssessment This course is assessed by an individual case study piece of coursework (30%) and a two hour unseen written examination (70%). Job RequirementA Ph.
D or Master's Degree in related discipline from a reputable university. Other
- At least 2 years of relevant teaching experience at the tertiary level is preferred- 5 years of relevant work experience will be an added advantage- Applicant must be able to teach day time classes. We regret that only shortlisted candidates will be notified.