Dimensionality Reduction: PCA & LDACovers PCA and LDA for dimensionality reduction, explaining variance maximization, eigenvector problems, and the benefits of Kernel PCA for nonlinear data.
Clustering: K-means & LDACovers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.
Machine Learning FundamentalsIntroduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
Machine Learning FundamentalsIntroduces the basics of machine learning, covering supervised classification, logistic regression, and maximizing the margin.
Data Representation: PCACovers data representation using PCA for dimensionality reduction, focusing on signal preservation and noise removal.
Clustering: Theory and PracticeCovers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.