Linear Models for Classification

Related lectures (30)

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Covers linear and logistic regression, including underfitting, overfitting, and performance metrics.

Explores linear models for classification, logistic regression, decision boundaries, SVM, multi-class classification, and practical applications.

Explores the extension of linear classifiers to handle multiclass problems and compares their performance on various datasets.

Introduces Support Vector Machines, covering Hinge Loss, hyperplane separation, and non-linear classification using kernels.

Explores linear models for classification, logistic regression, and gradient descent in machine learning.

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Covers the concept of gradient descent for linear regression, explaining the iterative process of updating parameters.

Covers supervised learning through likelihood maximization to find optimal parameters.

Covers linear models, binary classification, logistic regression, and model evaluation metrics.

Explores logistic regression for predicting vegetation proportions in the Amazon region through remote sensing data analysis.