Penalized Least Squares Methods
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Penalized Least Squares Methods
Penalized least squares methods are a class of techniques in statistics and machine learning that modify the traditional least squares approach to improve prediction accuracy and interpretability, especially when dealing with complex or high-dimensional data.
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Requirements
- Basic understanding of Ordinary Least Squares (OLS) method
General Overview
Description
This course provides an in-depth exploration of advanced penalized least squares methods, with a special focus on Lasso (Least Absolute Shrinkage and Selection Operator) and SCAD (Smoothly Clipped Absolute Deviation). We also provide the introduction of how Lasso works in constructing sytemic risk by explaining its role in Financial Risk Meter(https://ida.ase.ro/financial-risk-meter/). This course is for students with a background in statistics or machine learning, this course aims to equip participants with the knowledge and skills to apply these techniques effectively in high-dimensional data analysis, enhancing both predictive accuracy and interpretability.
Courses that include this CL
Meet the instructors !
Phd in Sun Yat-sen University visiting Phd in Humboldt University of Berlin