Abstract
Every time we search the Web, buy a product online, swipe a credit card, or even check our e-mail, we are using a sophisticated machine learning system, built on a massive cloud platform, driving billions of decisions every day. Machine learning has many paradigms. In this chapter, we explore the philosophical, theoretical, and practical aspects of one of the most common machine learning paradigms—supervised learning—that essentially learns a mapping from an observation (e.g., symptoms and test results of a patient) to a prediction (e.g., disease or medical condition), which in turn is used to make decisions (e.g., prescription). This chapter explores the process, science, and art of building supervised learning models.
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Notes
- 1.
https://archive.ics.uci.edu/ml/datasets/iris (Retrieved August 2, 2018).
- 2.
http://yann.lecun.com/exdb/mnist/ (Retrieved August 2, 2018).
References
Carbonell, J. G., Michalski, R. S., & Mitchell, T. M. (1983). An overview of machine learning. In R. S. Michalski, J. G. Carbonell, & T. M. Mitchell (Eds.), Machine learning volume 1. Symbolic computation (pp. 3–23). Berlin: Springer Science & Business Media.
Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning (Vol. 1, No. 10) Springer series in statistics. New York, NY: Springer.
Han, J., Pei, J., & Kamber, M. (2011). Data mining: Concepts and techniques. Amsterdam: Elsevier.
Li, L., Chu, W., Langford, J., & Wang, X. (2011). Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms. In WSDM’11 (Ed.), Proceedings of the Fourth ACM International Conference on Web Search and Data Mining (pp. 297–306). New York City, NY: ACM.
Murphy, K. (2012). Machine learning – A probabilistic perspective. Cambridge, MA: The MIT Press.
Singhal, A., Sinha, P., & Pant, R. (2017). Use of deep learning in modern recommendation system: A summary of recent works. International Journal of Computers and Applications, 180(7), 17–22.
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Kumar, S. (2019). Machine Learning (Supervised). In: Pochiraju, B., Seshadri, S. (eds) Essentials of Business Analytics. International Series in Operations Research & Management Science, vol 264. Springer, Cham. https://doi.org/10.1007/978-3-319-68837-4_16
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DOI: https://doi.org/10.1007/978-3-319-68837-4_16
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