An Introduction to Statistical Machine Learning using R:  Part II (October 4 and 6)

Abstract:

Statistical machine learning and data mining is an interdisciplinary research area which is closely related to statistics, computer sciences, engineering, and bioinformatics. Many statistical machine learning and data mining techniques and algorithms are very useful for various scientific areas. This short course will provide an overview of statistical machine learning and data mining techniques with applications to the analysis of real data. Supervised learning techniques will be covered, including penalized regression such as LASSO and its variants, support vector machines. The main emphasis will be on the analysis of real data sets from various scientific fields. The techniques discussed will be demonstrated in R.

Intended audience:

This course is intended for researchers who have some knowledge of statistics and want to be introduced to statistical machine learning and data mining, or practitioners who would like to apply statistical machine learning techniques to their problems.

Prerequisite:

Participants should be familiar with linear regression and basic statistical and probability concepts, as well as some familiarity with R programming. 


Part II Outline (R exercises will be included): October 4 & 6

Supervised Learning: Tree-based Methods


CART
Bagging
Random Forests
Unsupervised Learning: Dimension reduction

Principal Component Analysis
Other Dimension reduction Techniques
Unsupervised Learning: Clustering
K-means
Hierarchical Clustering
Other Selected Topics (if time permits)