MACHINE LEARNING
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
MACHINE LEARNING REFERENCES
Hastie, T., Tibshirani, R., & Friedman, J. H. (2008). The elements of statistical learning: Data mining, inference, and prediction. New York: Springer.
Mitchell, T. (1997). Machine Learning, New York: McGraw Hill.
Bishop, C. M. (2006) Pattern Recognition and Machine Learning. New York: Springer.
Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press.
Vapnik, V. N. (1998) Statistical Learning Theory. Wiley-Interscience.
Witten, I. H., Frank, E., Hall, M., A., & Pal, C. J. (2017). Data Mining: Practical Machine Learning Tools and Techniques (4th ed). New York: Morgan-Kaufmann.
Efron, B., & Hastie, T. (2017). Computer Age Statistical Inference: Algorithms, Evidence, and Data Science. Cambridge University Press.
Duda, R. O., Hart, P. E., & Stork, D. G. (2000). Pattern Classification (2nd ed). New York: Wiley-Interscience.
Barber, D. (2014). Bayesian Reasoning and Machine Learning. Cambridge University Press.
MACHINE LEARNING RESOURCES
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