Machine learning: Trends, perspectives, and prospects

@article{Jordan2015MachineLT,  title={Machine learning: Trends, perspectives, and prospects},  author={Michael I. Jordan and Thomas Mitchell},  journal={Science},  year={2015},  volume={349},  pages={255 - 260},  url={https://api.semanticscholar.org/CorpusID:677218}}
The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing.

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73 References

The elements of statistical learning: data mining, inference and prediction

This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering.

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

The Elements of Statistical Learning: Data Mining, Inference and Prediction, edited by Trevor Hastie, Robert Tibshirani and Jerome Friedman.

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Reinforcement Learning: An Introduction

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Computational and statistical tradeoffs via convex relaxation

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A theory of the learnable

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