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.

To teach the optimization formulations of various data mining techniques developed for the unsupervised learning, supervised learning, association rule mining and feature subset selection problems.

Lecture materials, including the class overheads, readings, assignments etc. will be available at the course web site prior to the lectures. Students are expected to check the web site regularly in order to attain the recently posted material.