3 units · CPE department course · Nigerian universities
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What you'll be able to do (official learning outcomes)
On the successful completion of this unit, students should be able to:
identify the characteristics of datasets and compare the trivial data and big data for various applications
select and implement machine learning techniques and computing environment that are suitable for the applications under consideration
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
understand and apply scaling up machine learning techniques and associated computing techniques and technologies
recognise and implement various ways of selecting suitable model parametres for different machine learning techniques;
integrate machine learning libraries and mathematical and statistical tools with modern technologies like hadoop and mapreduce.
Course contents
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.
Source: the Nigerian Universities Commission (NUC) Core Curriculum and Minimum Academic Standards (CCMAS). StudyOps loads this syllabus automatically when you study CPE 511.