Kernel Regression: Basics and Applications

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Page 2 of 4

Explores kernels for simplifying data representation and making it linearly separable in feature spaces, including popular functions and practical exercises.

Feature Expansion: Kernels and KNN

Covers feature expansion, kernels, and K-nearest neighbors, including non-linearity, SVM, and Gaussian kernels.

Nonparametric Regression

Covers nonparametric regression, scatterplot smoothing, kernel methods, and bias-variance tradeoff.

Introduction to Machine Learning: Linear Models

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.

Kernel Regression

Covers the concept of kernel regression and making data linearly separable by adding features and using local methods.

Kernel Methods: Efficient Dot Product Computation

Demonstrates efficient dot product computation and introduces nonlinear boundaries in the original space.

Kernel Methods

Covers overfitting, model selection, validation methods, kernel functions, and SVM concepts.

Vector Spaces: Linear Applications and Generators

Introduces vector spaces, linear applications, generators, and dimensionality in mathematics.

Untitled

Kernel Regression: Weighted Average and Feature Maps

Covers kernel regression and feature maps for data separability.