Supervised Learning: Regression Methods

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Page 2 of 4

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

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

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

Discusses decision trees and random forests, focusing on their structure, optimization, and application in regression and classification tasks.

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.

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.

Explores model assessment, hyperparameter tuning, and resampling strategies in machine learning.

Explores underfitting, overfitting, and the bias-variance trade-off in machine learning models.