Explores kernels for simplifying data representation and making it linearly separable in feature spaces, including popular functions and practical exercises.
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.
