Gaussian Naive Bayes & K-NN

Related lectures (28)

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Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.

Explores the Nearest Neighbor Rules, k-NN algorithm challenges, Bayes classifier, and k-means algorithm for clustering.

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.

Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.

Covers logistic regression's probabilistic interpretation, multinomial regression, KNN, hyperparameters, and curse of dimensionality.

Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.

Explores continuous random variables, density functions, joint variables, independence, and conditional densities.

Covers the basics of probability theory, random variables, joint probability, and inference.

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