Examining the capabilities of machine learning methods and ideas on how they apply to real-world problems, this text assesses machine learning, then introduces applications of ML techniques in fields such as data mining, knowledge discovery, human language technology, and user modelling.

Methods.- Comparing Machine Learning and Knowledge Discovery in DataBases: An Application to Knowledge Discovery in Texts.- Learning Patterns in Noisy Data: The AQ Approach.- Unsupervised Learning of Probabilistic Concept Hierarchies.- Function Decomposition in Machine Learning.- How to Upgrade Propositional Learners to First Order Logic: A Case Study.- Case-Based Reasoning.- Genetic Algorithms in Machine Learning.- Pattern Recognition and Neural Networks.- Model Class Selection and Construction: Beyond the Procrustean Approach to Machine Learning Applications.- Integrated Architectures for Machine Learning.- The Computational Support of Scientic Discovery.- Support Vector Machines: Theory and Applications.- Pre- and Post-processing in Machine Learning and Data Mining.- Machine Learning in Human Language Technology.- Machine Learning for Intelligent Information Access.- Machine Learning and Intelligent Agents.- Machine Learning in User Modeling.- Data Mining in Economics, Finance, and Marketing.- Machine Learning in Medical Applications.- Machine Learning Applications to Power Systems.- Intelligent Techniques for Spatio-Temporal Data Analysis in Environmental Applications.