Short course: Statistical Learning and Data Mining IV, NYC, Nov 23
Tags: Data Mining, Data Science, New York City, NY, Robert Tibshirani, Statistical Learning, Trevor Hastie
This new twoday course gives a detailed and modern overview of statistical models used by data scientists for prediction and inference, with emphasis on tools useful for tackling modernday data analysis problems.
Statistical Learning and Data Mining IV
StateoftheArt Statistical Methods for Data Science, including sparse models and deep learning
by Trevor Hastie and Robert Tibshirani,
Stanford University
Executive Conference Center, New York, NY
Nov 23, 2017
This new twoday course gives a detailed and modern overview of statistical models used by data scientists for prediction and inference. With the rapid developments in internet technology, genomics, financial risk modeling, and other hightech industries, we rely increasingly more on data analysis and statistical models to exploit the vast amounts of data at our fingertips.
In this course we emphasize the tools useful for tackling modernday data analysis problems. Many of these are essential building blocks, but we also include techniques at the cuttingedge of technology for handling bigdata problems. From the vast array of tools available, we have selected what we consider are the most relevant and exciting. Our list of topics includes:
The material is based on recent papers by the authors and other researchers, as well as our best selling book:
Elements of Statistical Learning: data mining, inference and prediction (2nd Edition) (with J. Friedman, SpringerVerlag, 2009).
The lectures will consist of highquality projected presentations and discussion. A copy of Elements of Statistical Learning will be given to all attendees, as well as a color booklet containing the course slides in a convenient twoup, doublesided format.
The authors have two other popular books that are also relevant to this course:
Go to wwwstat.stanford.edu/~hastie/sldm.html
for more information and online registration.
StateoftheArt Statistical Methods for Data Science, including sparse models and deep learning
by Trevor Hastie and Robert Tibshirani,
Stanford University
Executive Conference Center, New York, NY
Nov 23, 2017
This new twoday course gives a detailed and modern overview of statistical models used by data scientists for prediction and inference. With the rapid developments in internet technology, genomics, financial risk modeling, and other hightech industries, we rely increasingly more on data analysis and statistical models to exploit the vast amounts of data at our fingertips.
In this course we emphasize the tools useful for tackling modernday data analysis problems. Many of these are essential building blocks, but we also include techniques at the cuttingedge of technology for handling bigdata problems. From the vast array of tools available, we have selected what we consider are the most relevant and exciting. Our list of topics includes:
 Linear methods: regression, logistic regression (binary and multiclass), Cox model.
 Bootstrap, crossvalidation, and permutation methods.
 Regularized linear models: ridge, lasso, elastic net. Postselection inference. Glmnet package in R, and other software.
 Trees, random forests, and boosting.
 Unsupervised methods: clustering (prototype, hierarchical, spectral,...), principal components and other lowrank methods, sparse decompositions.
 Supportvector machines and kernel methods.
 Deep learning and neural networks.
The material is based on recent papers by the authors and other researchers, as well as our best selling book:
Elements of Statistical Learning: data mining, inference and prediction (2nd Edition) (with J. Friedman, SpringerVerlag, 2009).
The lectures will consist of highquality projected presentations and discussion. A copy of Elements of Statistical Learning will be given to all attendees, as well as a color booklet containing the course slides in a convenient twoup, doublesided format.
The authors have two other popular books that are also relevant to this course:
 An Introduction to Statistical Learning, with applications in R (with Gareth James and Daniela Witten, SpringerVerlag, 2013).
 Statistical Learning with Sparsity: the Lasso and Generalizations (with Martin Wainwright, Chapman and Hall, 2015).
Go to wwwstat.stanford.edu/~hastie/sldm.html
for more information and online registration.
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