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Are there any videos or other books/notes that anyone has come across that follow Pattern Recognition and Machine Learning by Chris Bishop? I bought this book to learn Machine Learning and am having some trouble getting through it.

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    $\begingroup$ Look for existing threads tagged with the references tag. $\endgroup$ Sep 6 '16 at 17:48
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    $\begingroup$ FWIW, I think the question is as on-topic as any other reference request. I actually think it's more specific than most because this question specifically asks for materials following a textbook, rather than just machine learning in general. $\endgroup$
    – Sycorax
    Sep 6 '16 at 21:28
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Bishop is a great book. I hope these suggestions help with your study:

  • The author himself has posted some slides for Chapters 1, 2, 3 & 8, as well as many solutions.
  • A reading group at INRIA have posted their own slides covering every chapter.
  • João Pedro Neto has posted some notes and workings in R here. (Scroll down to where it says "Bishop's Pattern Recognition and ML")
  • Many introductory machine learning courses use Bishop as their textbook. Googling gives a few different ones; have a look and see which topics and focus you prefer.
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I would recommend these resources to you:

  1. Tom Mitchell: Carnegie Mellon University
  2. (Only for Supervised Learning and follows Bishop) Pattern Recognition: Indian Institute of Science (I personally like this course as I have attended it, but this course requires you to know probability theory.)

Both the courses are maths oriented, for a lighter course on machine learning would be "Machine Learning" by Udacity

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https://www.cs.toronto.edu/~rsalakhu/STA4273_2015/

This course closely follows part of Bishop's. It has lecture videos with it.

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    $\begingroup$ Welcome to the site. At present this is more of a comment than an answer. You could expand it, perhaps by giving a summary of the information at the link, or we can convert it into a comment for you. $\endgroup$ Dec 2 '19 at 4:36
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jupyter notebooks with python implementations and scikit-learn usage at PRML

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I think an often overlooked book is Information Theory, Inference, and Learning Algorithms by David MacKay.

It follows the general framework of PRML, since the authors seem to have a similar (at least in my view) perspective. Depending on your background -- whether or not you enjoy concepts like information theory/coding/KL-divergence -- you may find this book extremely eye-opening.

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