Classification with GMM

Related lectures (29)

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Covers Gaussian Naive Bayes, K-nearest neighbors, and hyperparameter tuning in machine learning.

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

Explores Gaussian mixture models and denoising noisy signals using a probabilistic approach.

Covers classification using GMM and kNN, exploring boundaries, errors, and practical exercises.

Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.

Covers the Perceptron model, SGD, and Fisher's Linear Discriminant in binary classification.

Covers quantifying performance through true positives, false negatives, and false positives in machine learning.

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

Explores generalized linear regression, logistic regression, and multiclass classification in machine learning.