Classification with GMM

Related lectures (29)

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Explores Gaussian classifiers, texture classification, parameter estimation, supervised learning, and deep neural networks in image processing.

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

Covers linear classification using logistic regression, regularization, and multiclass classification.

Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.

Introduces decision trees and k-nearest neighbors for classification tasks, exploring metrics like accuracy and AUC.

Explores denoising signals with Gaussian mixture models and EM algorithm, EMG signal analysis, and image segmentation using Markovian models.

Explores logistic regression for binary classification, covering probability modeling, optimization methods, and regularization techniques.

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.