Multivariate Methods II: ICA for Functional Brain Imaging

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Explores Principal Component Analysis for dimensionality reduction and unsupervised feature selection.

Explores linear dimensionality reduction through PCA, variance maximization, and real-world applications like medical data analysis.

Explores the applications and limitations of Principal Component Analysis, including denoising, compression, and regression.

Covers unsupervised learning, focusing on dimensionality reduction and clustering, explaining how it helps find patterns in data without labels.

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

Explores Principal Component Analysis for dimension reduction and data representation in a new basis.

Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.

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

Explores unsupervised learning techniques for reducing dimensions in data, emphasizing PCA, LDA, and Kernel PCA.

Covers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.