Regularization: Tikhonov and Ridge

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Covers the probabilistic model for linear regression and the importance of regularization techniques.

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

Explores cross-validation, overfitting, regularization, and regression techniques in machine learning.

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

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

Compares L1 and L0 penalization in linear regression with orthogonal designs using greedy algorithms and empirical comparisons.

Explores kernel methods in machine learning, emphasizing their application in regression tasks and the prevention of overfitting.

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

Covers the basics of polynomial regression and regularization to prevent overfitting.

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