Nonparametric Regression: Kernel-Based Estimation

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Kernel Methods and Regression

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

Introduction to Machine Learning: Supervised Learning

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

Nonparametric Regression: Local Polynomial Estimation

Explores nonparametric regression using local polynomial estimation to balance data fidelity and smoothness.

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.

Nonparametric Statistics: Bayesian Approach

Explores non-parametric statistics, Bayesian methods, and linear regression with a focus on kernel density estimation and posterior distribution.

Gaussian Process Regression: Kernels and Comparisons

Explores Gaussian Process Regression kernels, computational costs, and comparisons with Ridge Regression and other non-linear regression techniques.

Data-Driven Modeling: Regression

Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.

Neural Network: Random Features and Kernel Regression

Covers random features in neural networks and kernel regression equivalence.

Kernel Methods

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

Nonlinear ML Algorithms

Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.