Machine Learning and Applications

On the successful completion of this unit, students should be able to: 1. identify the characteristics of datasets and compare the trivial data and big data for various applications; 2. select and implement machine learning techniques and computing environment that are suitable for the applications under consideration; 3. solve problems associated with batch learning and online learning, and the big data characteristics such as high dimensionality, dynamically growing data and in particular scalability issues; 4. understand and apply scaling up machine learning techniques and associated computing techniques and technologies; 5. recognise and implement various ways of selecting suitable model parametres for different machine learning techniques; and 6. integrate machine learning libraries and mathematical and statistical tools with modern technologies like hadoop and mapreduce.

Introduction to machine learning; ; introduction to R or Python for machine learning: statistics for analytics: descriptive statistics, inferential statistics, estimation and hypothesis testing, ANOVA; machine learning: unsupervised learning – clustering, supervised learning – classification, decision trees, random forest, and model performance measures.