This course leads the participants to analyze and discuss the general tasks and problems of statistical learning (machine learning), as well as their pitfalls. In this course, participants will be introduced to simple association rules, mining, classification, and clustering algorithms.

Following the course, the participants have the option of working on a guided project, getting feedback from the instructor.

Learning Outcomes

At the end of this course the participant will be able to:
  • Differentiate between situations which require a supervised learning approach and those which require an unsupervised approach (or some combination of both)
  • Identify strategies used to overcome common real-world statistical learning issues and challenges
  • Recognize the variety of machine learning algorithms available to them
  • Implement simple machine learning algorithms to provide actionable insights
  • Build a simple data analysis pipeline incorporating machine learning components

Additional Information

  • Participants are required to have a laptop/PC on which the current version of R/RStudio is installed (and for which they may require administrative authorisation to install packages).


12 hours

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Dr. Boily is a professor in the Mathematics Department and has offered upwards of 200+ training days in data science, analysis, visualization, and machine learning to 60+ cohorts of professionals in the Ottawa region since 2015.