Statistical learning : bagging, boosting, SVM, introduction to neural networks

Time:09:00:01/04/2019 to 17:00:02/04/2019

Venue/Location: C2-714, VIASM

Speaker: Vincent Lefieux, RTE, France.

This course is organized by The International Conference on Applied Probability and Statistics (CAPS2019), at https://caps2019.viasm.edu.vn/

Purpose: Statistical learning is a theoretical framework for machine learning, methods massively used by data scientists today. Lectures will be accompanied by some tutorials and computer lab works with R (or Python) software.

Schedule

	*Monday, 1^st April*	*Monday, 1^st April*	*Tuesday, 2^st April*	*Tuesday, 2^st April*
Stat.Learning	9AM – 12 PM	2PM – 5PM	9AM – 12 PM	2PM – 5PM

Programs:

- Bagging (random forest)
- Boosting
- SVM
- Introduction to neural networks

Pre-requisites:

-Linear regression and logistic regression

-Basis concept of software R (or Python).

References:

-Breiman, L. (1996). Bagging predictors. Machine Learning, 24:123-140.

-Breiman, L. (2001). Random forests. Machine Learning, 45:5-32.

-Hastie, T., Tibshirani, R., and Friedman, J. (2009). The elements of statistical learning. Data Mining, inference, and prediction. Springer Series in Statistics. Springer, 2nd edition.

-James, G., Witten, D., Hastie, T. and Tibshirani, R. (2013) An Introduction to Statistical Learning, with Applications in R. Springer.