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.
• The official syllabus for Stat 406 is as follows:
```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.
```

• The official syllabus for Stat 306 is as follows:
```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.
```

• The official syllabus for CS340 is as follows:
```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.
```
The bottom line is after having taken CS340/Stat306 + Stat406, everyone should know the following [Bishop chapter numbers in brackets]
• Clustering (mixtures of Gaussians, EM) [B9]
• Dimensionality reduction (PCA) [B12]
• Linear regression [B3]
• Logistic regression [B4]
• Naive Bayes classifiers [B4]
• Some nonlinear regression and classification techniques, e.g., CART [B14.4] , neural nets [B5], SVMs [B7]