Trevor Hastie is the John A. Overdeck Professor of Statistics and Professor of Biomedical Data Science at Stanford University. He has a joint appointment in the Department of Statistics at Stanford University, and the Division of Biostatistics of the Health, Research and Policy Department in the Stanford School of Medicine. Professor 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.
Topic and course outline
We give an 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 high-tech industries, we rely increasingly more on data analysis and statistical models to exploit the vast amounts of data at our fingertips.
We then focus on several important classes of tools. For wide data, we have a closer look at the lasso and its relatives, and for tall data random forests and boosting. We also review the recent advances in deep learning. Most of the material can be found in “An Introduction to Statistical Learning, with Applications in R” by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani (Springer, 2013), which is also available free in pdf format here.
Keywords: machine learning, statistical models, neural networks, lasso
Workshop 12 May 2017
Title: “Statistical Learning and Econometrics”
Keynote speaker on this workshop is Trevor Hastie (Stanford University), other Speakers are:
– Arthur Charpentier (Université de Rennes)
– Jason Roos (Rotterdam School of Management, Erasmus University)
– David Martens (Universiteit Antwerpen)
– Didier Nibbering (Erasmus School of Economics)
– Gerard Biau (Université Pierre et Marie Curie, Paris)
For academic participants there is no fee.
For more information on the Workshop:
Tinbergen Institute Rotterdam, Burg. Oudlaan 50, 3062 PA Rotterdam