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Machine learning is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behavioral strategies based on empirical data. As such, machine learning encompasses all data mining methods that can be implemented as an automated process. Machine learning includes a number of advanced statistical methods for handling regression and classification tasks with multiple dependent and independent variables. Examples are support vector machines (SVM) for regression and classification, naive Bayes for classification, and k-nearest neighbors (KNN) for regression and classification. In addition to that, machine learning encompasses unsupervised learning algorithms, cluster analysis in particular.

**MACHINE LEARNING SUBCATEGORIES**

Mitchell, T. (1997). Machine Learning, New York: McGraw Hill.

Bishop, C. M. (2006) Pattern Recognition and Machine Learning. New York: Springer.

Vapnik, V. N. (1998) Statistical Learning Theory. Wiley-Interscience.

Hastie, T., Tibshirani, R., & Friedman, J. H. (2008). The elements of statistical learning: Data mining, inference, and prediction. New York: Springer.

Han, J., Kamber, M. (2000). Data mining: Concepts and Techniques. New York: Morgan-Kaufman.

Weiss, S. M., & Indurkhya, N. (1997). Predictive data mining: A practical guide. New York: Morgan-Kaufman.

Witten, I. H., & Frank, E. (2000). Data mining. New York: Morgan-Kaufmann.

Berry, M., J., A., & Linoff, G., S., (2000). Mastering data mining. New York: Wiley.

- Data Mining Resources, Dept. of Computer Science, Purdue University Data Mining Resources

- Data Sets for Data Mining, School of Informatics, University of Edinburgh
- Directory of Public Data Mining Software, DMOZ

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