Overview
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Learn about advanced regression techniques in this comprehensive data mining lecture that progresses from linear regression fundamentals to complex nonlinear methods and regularization approaches. Begin with a quick review of linear regression before diving into nonlinear regression concepts, including practical Python implementations and various nonlinear mapping examples. Explore polynomial regression unification, feature space mapping, and optimization techniques for nonlinear models. Address critical machine learning challenges like underfitting and overfitting through cross-validation methods. Conclude with an in-depth examination of regularization principles, different types of regularized regression models, and specific solutions for ridge regression problems.
Syllabus
Recording starts
Announcements
Linear regression recap
Nonlinear regression intro
Linear regression python
Examples of nonlinear mappings
Unifying polynomial regression
Mapping to feature space
Optimizing nonlinear regression
Underfitting / overfitting
Cross validation
Regularization
Regularized regressions
Solution to ridge regression
Taught by
UofU Data Science