95-801 Data Mining Techniques

6 units, Carnegie Mellon University

Knowledge discovery in data is the nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data Fayyad et al. 1996 Motivation Data generated by humans and machines is available everywhere and growing steadily. In todays datadriven world it is crucial for students to acquire the fundamental skills for being able to analyze massive datasets and to develop datadriven techniques toward solving realworld problems. This course will cover the fundamental concepts and techniques in data mining, and equip students with the basic skillset toward becoming good data scientists. Major topics include algorithms and tools for data exploration, fast similarity search, pattern mining, outlier detection, dimensionality reduction, ranking, and recommender systems. The coursework involve miniprojects on various datasets to enable students to gain handson experience with data analytics. For course related details syllabus, assignments, etc. see httpwww.andrew.cmu.eduuserlakoglucourses95801index.htm

Prerequisites: none listed

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