Introduces the basics of machine learning, covering supervised classification, decision boundaries, and polynomial curve fitting.
Covers data streams, sub-linear memory computation, document similarity, and randomized dimension reduction techniques for handling 'Big Data' challenges efficiently.
Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.
Covers data representation using PCA for dimensionality reduction, focusing on signal preservation and noise removal.
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.
