Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Learn about distance metric learning and outlier detection in this university lecture that covers essential data mining concepts. Explore the fundamentals of distance metric learning, including optimization objectives and procedures, before diving into DML-EIG implementation. Understand different classes of data noise and various approaches to outlier detection, including removal techniques and density-based methods. Master the DBSCAN algorithm and its applications, followed by an exploration of reverse nearest neighbors. Conclude with an introduction to matrix completion techniques. The lecture provides both theoretical foundations and practical implementations of these crucial data mining concepts.