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.
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.
I would recommend these resources to you:
- Tom Mitchell: Carnegie Mellon University
- (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
This course closely follows part of Bishop's. It has lecture videos with it.
jupyter notebooks with python implementations and scikit-learn usage at PRML
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.