The data mining course provides background information on the design and use of data mining algorithms and applications in data mining on the web, computational biology and various medical applications. Topics include models, methods and processes of data mining including search and querying, data dredging and fishing, discrete structures involving item-set mining, concept lattices, condensed representation, frequent pattern mining, customized data structures for speeding up data mining algorithms, attribute-value learning techniques including decision tree, decision lists, classification and regression trees, association rules, and rule-based mining, relational mining techniques, probabilistic techniques including conditional independence and its modeling, representational complexity, Bayesian networks, and probabilistic models for query approximation, sequences and order, compositional data mining, mining chains of relations, integrated query and mining languages, paradigms for interfacing with database systems, and application in bi-informatics, personalization, information retrieval, web modeling, filtering and text processing.