Trevor Hastie

 

 


 

Trevor Hastie is the John A Overdeck Professor of Statistics at Stanford University. Hastie is known for his research in applied statistics, particularly in the fields of data mining, bioinformatics and machine learning. He has published four books and over 180 research articles in these areas. Prior to joining Stanford University in 1994, Hastie worked at AT&T Bell Laboratories for 9 years, where he helped develop the statistical modeling environment popular in the R computing system. He received his B.S. in statistics from Rhodes University in 1976, his M.S. from the University of Cape Town in 1979, and his Ph.D from Stanford in 1984. Professor Hastie is an elected fellow of the Institute of Mathematical Statistics, the American Statistical Association, the International Statistics Institute, the South African Statistical Association and the Royal Statistical Society. He has received a number of awards and honors, including the Myrto Lefkopolous award from Harvard in 1994.

More info: http://www.stanford.edu/~hastie/




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Jan 12th 2016

This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).

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