Basic principles of machine learning (estimation, optimization, prediction, generalization, bias-variance trade-off, regularization) in the context of supervised (linear models, decision trees, deep neuronal networks) and unsupervised (clustering and dimensionality reduction) statistical learning techniques. The course emphasizes the ability to apply techniques to real data sets and critically evaluate their performance.
Antirequisite(s): the former Computer Science 4414A/B, the former Statistical Sciences 3850F/G, the former Software Engineering 4460A/B.
Extra Information: 2 lecture hours/week, 2 lab hour/week. For a full list of Introductory Statistics courses please see: https://www.westerncalendar.uwo.ca/Departments.cfm?DepartmentID=55&SelectedCalendar=Live&ArchiveID=
Course Weight: 0.50
Subject Code: DATASCI
This Course is Mentioned in the Following Calendar Pages: