DATASCI 3000A
0.50 credit · Main
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.
(Data Science 1200A/B or Computer Science 1026A/B or Computer Science 1027A/B or Computer Science 2120A/B or Digital Humanities 2220A/B or Engineering Science 1036A/B or Data Science 2000A/B or Integrated Science 2002B or Statistical Sciences 2864A/B); (Data Science 2000A/B or Integrated Science 2002B or Statistical Sciences 2857A/B or 0.5 course from the Introductory Statistics Course List); (Mathematics 1600A/B or Numerical and Mathematical Methods 1411A/B or the former Applied Mathematics 1411A/B or Data Science 2100A); (Calculus 1000A/B or Calculus 1500A/B or Numerical and Mathematical Methods 1412A/B or the former Applied Mathematics 1412A/B or Data Science 2100A). Note that Data Science 2000A/B, Integrated Science 2002B and Data Science 2100A can be used to fulfill multiple prerequisites.
the former Computer Science 4414A/B, the former Statistical Sciences 3850F/G, the former Software Engineering 4460A/B.
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=
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