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Related lectures (31)

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Covers generative models with a focus on self-attention and transformers, discussing sampling methods and empirical means.

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

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

Covers deep learning fundamentals, focusing on multilayer perceptrons and their training processes.

Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.

Explores autoencoders, from linear mappings in PCA to nonlinear mappings, deep autoencoders, and their applications.

Explores PCA and t-SNE for reducing dimensions and visualizing high-dimensional data effectively.

Explores textual data classification, focusing on methods like Naive Bayes and dimensionality reduction techniques like Principal Component Analysis.

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Covers dimensionality reduction, clustering, and density estimation techniques, including PCA, K-means, GMM, and Mean Shift.