Supervised learning

Related lectures (31)

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Explores underfitting, overfitting, hyperparameters, bias-variance trade-off, and model evaluation in machine learning.

Explores unsupervised learning through clustering techniques, algorithms, applications, and challenges in various fields.

Covers supervised learning, classification, regression, decision boundaries, overfitting, Perceptron, SVM, and logistic regression.

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

Offers insights into the statistical physics of learning, exploring the relationship between neural network structure and disordered systems.

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

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

Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.

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

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