Agenda Day Three

 

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Multivariate Insights with Machine Learning and Unsupervised Learning

Day three will conclude the seminar with a discussion of tree-based models, as well as “unsupervised learning” techniques, such as principal components analysis (PCA) and clustering. Each of these techniques can be used profitably in isolation or in conjunction with core techniques like GLM. Tree-based modeling will be illustrated with the classification and regression trees (CART) algorithm. Other machine learning techniques like multivariate adaptive regression splines (MARS) may also be covered as time allows. Clustering and PCA will be discussed as a complementary set of unsupervised learning techniques, which is useful for gaining insights into high-dimensional datasets. The topics will be covered at a high conceptual level, with an emphasis on practical case studies.

Thursday, March 29
7:30 a.m. – 9:30 a.m.
  • Recursive partitioning, splitting rules, measures of purity and “pruning back” using cross-validation
  • Case study

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9:30 a.m. – 11:00 a.m.
  • Principal components analysis (PCA): fundamental concepts
  • k-means clustering: the central idea
  • Case study illustrating principal components and clustering analysis

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11:00 a.m. – 11:30 a.m.
  • Q&A and open discussion

Session Coordinator(s)

Facilitator(s)