Lectures on Advanced Regression Methods
Thời gian: 14:00 đến 17:00 ngày 24/07/2017, 09:00 đến 12:00 ngày 25/07/2017, 14:00 đến 17:00 ngày 26/07/2017, 09:00 đến 12:00 ngày 27/07/2017, 14:00 đến 17:00 ngày 28/07/2017,
Địa điểm: C2-714, VIASM
Mục đích: Data scientists have at their disposal many regression techniques in predictive modeling. If the most well known is the (Gaussian) linear regression, other methods of regression can be performed: Ridge, Lasso and ElasticNet Regression, MARS, GAM, trees, SVR. . . Lectures will be accompanied by tutorials and computer lab works with R software.
Báo cáo viên: Vincent Lefieux, RTE Paris, France.
Tóm tắt:Schedule.
Program.
• Reminders on linear regression (including choice of predictive variables).
• Ridge, Lasso and ElasticNet Regression.
• Multivariate Adaptive Regression Splines (MARS).
• Generalized Additive Model (GAM).
• Regression trees and random forests.
• Support Vector Machine Regression (SVR).
Language. English.
Prerequisites.
• Probability.
• Linear Models.
• Inferential Statistics.
• Basic concepts of R software.
References.
• Breiman, L. (1996). Bagging predictors. Machine Learning, 24:123-140.
• Breiman, L. (2001). Random forests. Machine Learning, 45:5-32.
• Breiman, L., Friedman, J., Stone, C. J., and Olshen, R. (1984). Classification And Regression Trees. Taylor & Francis.
• Hastie, T. and Tibshirani, R. (1990). Generalized additive models. CRC. Chapman & Hall.
• Hastie, T., Tibshirani, R., and Friedman, J. (2009). The elements of statistical learning. Data Mining, inference, and prediction. Springer Series in Statistics. Springer, 2 edition.
• Wood, S. (2006). Generalized additive models: an introduction with R. CRC. Chapman & Hall.