Basic concepts of machine learning and data mining are introduced. Machine learning algorithms extract meaningful relationships from data. These relationships an in turn be used for building autonomous. For example, based on the prior engineering data, equipment faults can detected, the number of items to be ordered can be predicted, optimal control parameters can be determined. In pharmaceutical applications, the new knowledge can be applied for customized drug labeling, predicting adverse drug reactions, and to increase understanding of genetic data for the benefit of new drug development. Data farming methods and their relationship with data mining are discussed. Numerous examples of industrial, medical, and pharmaceutical applications are discussed.

Dr. Andrew Kusiak is a Professor in the Department of Mechanical and Industrial Engineering at the University of Iowa in Iowa City, Iowa. He is interested in computational intelligence, data mining, innovation, automation, manufacturing, product development, pharma industry, and healthcare. Dr. Kusiak has published numerous books and technical papers in journals sponsored by professional societies, such as AAAI, ASME, IEEE, IIE, ESOR, IFIP, IFAC, INFORMS, ISPE, and SME. He speaks frequently at international meetings, conducts professional seminars, and consults for industrial corporations. Dr. Kusiak serves on editorial boards of over thirty journals, edits book series, and is the Editor-in-Chief of the Journal of Intelligent Manufacturing.