• Graduate program
    • Why Tinbergen Institute?
    • Program Structure
    • Courses
    • Course Registration
    • Recent PhD Placements
    • Admissions
    • Facilities
  • Research
  • News
  • Events
    • Events Calendar
    • Tinbergen Institute Lectures
    • Annual Tinbergen Institute Conference
    • Events Archive
    • Summer School
      • Econometric Methods for Forecasting and Data Science
      • Introduction in Genome-Wide Data Analysis
      • Business Data Science Summer School Program
  • Times
Home | Events Archive | Statistical Learning and Data Science
Tinbergen Institute Lectures

Statistical Learning and Data Science


  • Series
    Array
  • Speaker
    Trevor Hastie (Stanford University)
  • Field
    Data Science
  • Location
    Rotterdam
  • Date

    May 10, 2017 until May 12, 2017

Trevor Hastie is the John A. Overdeck Professor of Statistics and Professor of Biomedical Data Science at Stanford University. 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.

Lecture topic

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