Regularization in Machine Learning

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Page 3 of 3

Introduces the fundamentals of statistical learning, covering supervised learning, decision theory, risk minimization, and overfitting.

Model Complexity and Overfitting in Machine Learning

Covers model complexity, overfitting, and strategies to select appropriate machine learning models.

Probabilistic Models for Linear Regression

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Nonlinear ML Algorithms

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

Polynomial Regression and Gradient Descent

Covers polynomial regression, gradient descent, overfitting, underfitting, regularization, and feature scaling in optimization algorithms.

Ridge Regression: Regularizing Linear Models

Introduces ridge regression to regularize linear models and the kernel trick for nonlinear regression.

Flexibility of Models & Bias-Variance Trade-Off

Delves into the trade-off between model flexibility and bias-variance in error decomposition, polynomial regression, KNN, and the curse of dimensionality.

Polynomial Regression: Overview

Covers polynomial regression, flexibility impact, and underfitting vs overfitting.

Nonparametric Regression

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