Welcome to the Bishop-PRML-Resources repository! This repository is designed to provide you with additional learning resources and materials to supplement your study of "Pattern Recognition and Machine Learning" by Chris Bishop.
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The author himself has shared slides for Chapters 1, 2, 3 & 8, as well as many solutions.
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The INRIA reading group has also created comprehensive slides covering every chapter of the book. Access their slides here.
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João Pedro Neto has posted helpful notes and workings in R. Check them out here. (Scroll down to where it says "Bishop's Pattern Recognition and ML")
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(Only for Supervised Learning and follows Bishop) Pattern Recognition: Indian Institute of Science
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For a lighter introduction to machine learning, you can consider the "Machine Learning" course by Udacity.
A course that closely follows parts of Bishop's book is available at this link. It includes lecture videos that can enhance your understanding of the material.
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If you prefer hands-on learning, you can explore the collection of Jupyter notebooks with Python implementations and scikit-learn usage for PRML. Access the notebooks here.
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Additionally,you may find it helpful to explore the GitHub repository here, which contains Python code implementations of the algorithms described in Bishop's book.
An often overlooked book that aligns well with the framework of PRML is Information Theory, Inference, and Learning Algorithms by David MacKay. It offers a similar perspective and can be highly enlightening, especially if you enjoy concepts like information theory, coding, and KL-divergence.
The textbook "Pattern Recognition and Machine Learning" by Chris Bishop can be found here.