Statistical Learning and Data Science

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