The course will address unsupervised learning, supervised learning, association rule mining and feature subset selection problems, focus on the optimization formulations of these problems, discuss various techniques proposed as solutions and present their implementation particularly in the context of operations management. Among others, probabilistic and statistical methods, possibilistic methods clustering algorithms, decision trees, metaheuristics (such as genetic algorithms, simulated annealing, etc.) and mathematical programming will be covered as part of the toolbox that are widely utilized in data mining. As part of the course multi criteria decision making and multi objective optimization, and their usage in data mining will also be covered. The course will include case studies from both manufacturing and service industries.