Linear Models for Classification

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Linear Models for Classification

Covers linear models for classification, including SVM, decision boundaries, support vectors, and Lagrange duality.

Linear Models for Classification: Multi-Class Extensions

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.

Linear Models: Classification Basics

Explores linear models for classification, logistic regression, SVM, k-NN, and curse of dimensionality.

Support Vector Machines: Basics and Applications

Covers the basics of support vector machines, logistic regression, decision boundaries, and the k-Nearest Neighbors algorithm.

Linear Models & k-NN

Covers linear models, logistic regression, decision boundaries, k-NN, and practical applications in authorship attribution and image data analysis.

Linear Models for Classification: Logistic Regression and SVM

Covers linear models for classification, focusing on logistic regression and support vector machines.

Linear Models for Classification: Part 3

Explores linear models for classification, including binary classification, logistic regression, decision boundaries, and support vector machines.

Linear Models for Classification

Explores linear models for classification, including parametric models, regression, and logistic regression, along with model evaluation metrics and maximum margin classifiers.

Linear Models: Classification

Explores linear models for classification, including logistic regression, decision boundaries, and support vector machines.

Linear Classification Models: From Binary to Multiclass

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