Covers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.
Covers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.
Covers dimensionality reduction, clustering, and density estimation techniques, including PCA, K-means, GMM, and Mean Shift.
Covers feature extraction, clustering, and classification methods for high-dimensional datasets and behavioral analysis using PCA, t-SNE, k-means, GMM, and various classification algorithms.
Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.
Explores generalization error in machine learning, focusing on data distribution and hypothesis impact.
