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Machine Learning

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Abstract

It is common to encounter terminology such as, neural networks, deep learning and reinforcement learning, all of which are a form of machine learning. There are two major kinds of machine learning tasks: classification and regression.

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Notes and References

  1. Mitchell, Tom M. (1997). Machine Learning, McGraw-Hill.

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  2. McCulloch, W. S. and Pitts, W. (1943). A Logical Calculus of Ideas Immanent in Nervous Activity, Bulletin of Mathematical Biophysics. 5(4): 115–133.

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  3. Minsky, M. L. (1954). Theory of Neural-Analog Reinforcement Systems and Its Application to the Brain-Model Problem. Ph.D. thesis, Princeton University.

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  4. Rosenblatt, F. (1958). The Perceptron, a Probabilistic Model for Information Storage and Organization in the Brain, Pscyh. Review, 62: 386.

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  5. Minsky, M. and Papert, S. (1969). Perceptrons, MIT Press.

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  6. Fortmann-Roe, S. (2012). Understanding the Bias-Variance Tradeoff, http://scott.fortmann-roe.com/docs/BiasVariance.html.

  7. Hastie, T. et al. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd Ed, Springer.

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Correspondence to Mark Skilton .

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Skilton, M., Hovsepian, F. (2018). Machine Learning. In: The 4th Industrial Revolution. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-62479-2_5

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