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I am planing to change my field (in PhD) and learn Machine Learning to differentiate different phases of strongly correlated matter. I learned Monte Carlo method in my MS and have intermediate level knowledge of topological insulators.

Before completely getting into Machine Learning, I want to go-through an introductory level book/article of Machine learning for physicists. I want to know if it is too difficult for me to learn. (is it really very difficult?)

Do you know any books/articles in which Machine Learning is explained in context of Physics?

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  • $\begingroup$ I don't have any idea of Machine Learning and I work with simple model systems so that analytical results can be obtained either perturbatively or exactly. But this paper is tempting to be a good read. $\endgroup$ – Sunyam Jun 18 at 21:06
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the following book might help you It covers the following

  1. Supervised Machine Learning Algorithms
  2. Unsupervised Machine
  3. Learning Algorithms
  4. Semi Supervised Machine Learning Algorithms
  5. Random Forest Algorithms
  6. Support Vector Machine Algorithms
  7. Artificial Neural Network Algorithms
  8. Regression Algorithms
  9. Clustering Algorithms
  10. Resampling Algorithms
  11. Association Rule Algorithm

Reference Machine Learning: An overview with the help of R software ISBN: 978-1790122622 https://books.google.com/books/about/Machine_Learning_An_overview_with_the_he.html?id=NDF7DwAAQBAJ

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