MBI Course on Statistical Learning
The course covers basic concepts of modern statistical learning theory. The theory itself is born out of the challenge of understanding vast amounts of data routinely collected in modern science and has led to the development of new tools in the field of statistics, as well as has spawned new computer-assisted areas of research, such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often described with different terminology. This course attempts to collect some main ideas of statistical learning into a common conceptual framework appropriate for the audience with mathematical background.
| Topic | Comments |
| Introduction and overview | ELS2 Chap 1,2 |
| Computational methods for regression | ELS2 Chap 3 |
| Linear and kernel-based classification | ELS2 Chap 4,6 |
| Model assessment and selection | ELS2 Chap 7 |
| Tree based models and neural nets | ELS2 Chap 9, 11 |
| Random forests and ensemble learning | ELS2 Chap 16 |
| High dimensional data | ELS2 Chap 18 |
ELS2 - Elements of Statistical Learning, Second Edition by T. Hastie R. Tibshirani, and J. H. Friedman
Additional text with R code examples - An Introduction to Statistical Learning with Applications in R, by James Witten, Hasite, and Tibshirani
