Supervised Learning: Formalization and Cost Functions

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Explores binary classification by regression, decision functions, and various cost functions.

Covers supervised learning, decision theory, risk minimization, and goal achievement.

Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Explores models, learning paradigms, and applications in Mathematics of Data.

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Explores image classification using decision trees and random forests to reduce variance and improve model robustness.

Covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and classification.

Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.

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