The syllabus for Stat 406 has not yet been determined in detail. One issue is that some people have Stat 306 as a prereq and others havve CS 340. Also, the content of these courses varies significantly depending on who teaches Stat306 (Michael Schulzer or Will Welch), and CS340 (Kevin Murphy or Nando de Freitas). Below we list the official version of the story.
Flexible, data-adaptive methods for modeling large data sets: visualization and summarization of data; handling large data sets; robust regression and smoothing; methods for assessing accuracy of prediction; neural networks; classification and regression trees; nearest-neighbour methods; model averaging.
Modeling a response (output) variable as a function of several explanatory (input) variables: multiple regression for a continuous response, logistic regression for a binary response, and log-linear models for count data. Finding low-dimensional structure: principal components analysis. Cluster analysis.
Models of algorithms for dimensionality reduction, nonlinear regression, classification, clustering and unsupervised learning; applications to computer graphics, computer games, bio-informatics, information retrieval, e-commerce, databases, computer vision and artificial intelligence.