| dc.description.abstract | Over the past 8 years, the topics associated with statistical learning have been
expanded and consolidated. They have been expanded because new problems have
been tackled, new tools have been developed, and older tools have been refined.
They have been consolidated because many unifying concepts and themes have
been identified. It has also become more clear from practice which statistical
learning tools will be widely applied and which are likely to see limited service. In
short, it seems this is the time to revisit the material and make it more current.
There are currently several excellent textbook treatments of statistical learning
and its very close cousin, machine learning. The second edition of Elements of
Statistical Learning by Hastie, Tibshirani, and Friedman (2009) is in my view still
the gold standard, but there are other treatments that in their own way can be
excellent. Examples include Machine Learning: A Probabilistic Perspective by
Kevin Murphy (2012), Principles and Theory for Data Mining and Machine
Learning by Clarke, Fokoué, and Zhang (2009), and Applied Predictive Modeling
by Kuhn and Johnson (2013). | en_US |
