Non-parametric Regression: Smoothing Techniques

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

Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of feature expansion and kernel methods.

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

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Introduces the basics of supervised machine learning, covering types, techniques, bias-variance tradeoff, and model evaluation.

Explores advanced concepts in linear regression models, including multicollinearity, hypothesis testing, and handling outliers.

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

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

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Covers multicollinearity, outliers, model specification, and practical strategies in linear regression.