Kernel Regression

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Related lectures (31)

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Kernel Regression: K-nearest Neighbors

Covers the concept of kernel regression and K-nearest neighbors for making data linearly separable.

Kernel Methods: SVM and Regression

Introduces kernel methods like SVM and regression, covering concepts such as margin, support vector machine, curse of dimensionality, and Gaussian process regression.

Machine Learning Fundamentals

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

Feature Expansion and Kernel Methods

Explores feature expansion, kernel methods, SVM, and nonlinear classification in machine learning.

Kernel Methods and Regression

Covers kernel methods, kernel regression, RBF kernel, and SVM for classification.

Kernel Methods: Neural Networks

Covers the fundamentals of neural networks, focusing on RBF kernels and SVM.

Introduction to Machine Learning: Linear Models

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

Feature Expansion and Kernels

Covers feature expansion, kernels, SVM, and nonlinear classification in machine learning.

Nonparametric Regression

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

Kernel Regression: Basics and Applications

Explores kernel regression, the curse of dimensionality, and random features in neural networks.