Principal Component Analysis: Dimension Reduction

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Covers data representation using PCA for dimensionality reduction, focusing on signal preservation and noise removal.

Covers unsupervised learning, dimensionality reduction, SVD, low-rank estimation, PCA, and Monte Carlo Markov Chains.

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

Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

Introduces the fundamentals of statistical learning, covering supervised learning, decision theory, risk minimization, and overfitting.

Introduces artificial neural networks, CNNs, and dimensionality reduction using PCA and autoencoders.

Covers unsupervised learning with a focus on Principal Component Analysis and the Singular Value Decomposition.

On PCA includes interactive exercises and emphasizes minimizing information loss.

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