Understanding AutoencodersExplores autoencoders, from linear mappings in PCA to nonlinear mappings, deep autoencoders, and their applications.
Data Representation: PCACovers data representation using PCA for dimensionality reduction, focusing on signal preservation and noise removal.
Multivariate Methods IExplores multivariate methods like PCA, SVD, PLS, and ICA for dimensionality reduction in functional brain imaging.
PCA: Key ConceptsCovers the key concepts of PCA, including reducing data dimensionality and extracting features, with practical exercises.
PCA: Key ConceptsCovers the key concepts of Principal Component Analysis (PCA) and its practical applications in data dimensionality reduction and feature extraction.
Dimensionality ReductionIntroduces artificial neural networks and explores various dimensionality reduction techniques like PCA, LDA, Kernel PCA, and t-SNE.
Linear Dimensionality ReductionExplores linear dimensionality reduction through PCA, variance maximization, and real-world applications like medical data analysis.
