Data Mining - Fall 2025

Explore fundamental concepts and advanced techniques in data mining through this comprehensive university lecture series from the University of Utah's Fall 2025 course taught by Instructor Jeff Phillips. Master essential topics including statistical principles, distance metrics, nearest neighbor search, Jaccard similarity, locality-sensitive hashing, hierarchical agglomerative clustering, k-means clustering algorithms, streaming data processing, frequency approximation methods, principal component analysis, matrix sketching, metric learning, random projections, machine learning theory, outlier detection, anomaly identification, privacy considerations, graph embeddings, and Markov chains. Gain practical knowledge of streaming algorithms for distinct item counting and mergability, while developing a solid foundation in both theoretical concepts and real-world applications of data mining techniques used in modern data science workflows.