Anyone who recommends a book on Machine Learning and deep Learning should have heard of a classic textbook: Pattern Recognition and Machine Learning, or PRML for short. It’s the brainchild of Christopher Bishop, lab director at Microsoft Research Cambridge. Yes, it’s the one with 9.5 points on Douban. Maybe you have seen other public accounts before, but don’t worry, this tweet is a more comprehensive summary of “Pattern Recognition and Machine Learning” related Learning materials, you want and you didn’t think you want, here. Python code, official MATLAB code, Chinese translation, after-class answers, PPT, corresponding university video, study notes, xiaobian are summarized, anyway, I first collect a ~

If you want to get the full PDF you can get it \

How to obtain:

1. Follow the official account of “Deep Learning Algorithms and Computer Vision”

2. Reply to [PRML] in the background of [Deep Learning Algorithm and Computer Vision] official account.

???? Long press the qr code above2Reply "PRML" in seconds to obtain informationCopy the code

It is no exaggeration to say that PRML is the bible of AI. PRML has a wide range of coverage, popular language, more detailed examples and exercises, more basic explanation and guidance, and more reasonable difficulty gradient setting, which are the reasons why it is loved by middle-aged and elderly PhD friends.

It would be fair to call Bishop’s PRML the Bible of machine learning, a systematic introduction to the detailed concepts and fundamentals of pattern recognition and machine learning. The book provides an introduction to the basics of probability theory, as well as advanced linear algebra and multiple calculus. It is suitable for university graduate students and artificial intelligent-related practitioners.

PRML is rich in content, with 14 chapters, and each chapter is full of dry goods. The overall catalog is as follows:

  • Chapter 1 Introduction
  • Chapter two probability distribution
  • Chapter 3 linear regression model
  • Chapter 4 linear classification model
  • Chapter 5 Neural network
  • Chapter 6 Kernel methods
  • Chapter 7 Sparse kernel machines
  • Chapter 8 Graphic Model
  • Chapter 9 Mixed Model and EM
  • Chapter x Approximate inference
  • Chapter 11 Sampling method
  • Chapter 12 Continuous latent variables
  • Chapter xiII Sequential Data
  • Chapter 14 Combination model

In addition, zhihu’s discussion on “Why PRML is a classic among the classic books on machine learning?” “May give you some inspiration:

Answer by Luau Lawrence

www.zhihu.com/question/35…

Python3’s classic Machine Learning book Pattern Recognition and Machine Learning has been updated on GitHub by CTGK. It has been available in Matlab before, but the new version is implemented with python code, the most popular in machine learning, which is more suitable for people to use. The most important thing is that the code is presented as a Jupyter Notebook, so the visual result is a great way to debug code while reading a book.

MLPR Python code:

github.com/ctgk/PRML

If you want to get the full PDF you can get it \

How to obtain: \

1. Pay attention to the “programmer Baymax” public account

2. Reply to [PRML] in the background of [Programmer Dabai] official account.

???? Long press the qr code above2Reply "PRML" in seconds to obtain informationCopy the code

In addition, the official release of the corresponding Matlab version of the code:

github.com/PRML/PRMLT

▌ PRML books

  • Address: www.6aiq.com/article/158…
  • The copyright of this article belongs to both the author and AIQ. You are welcome to reprint this article, but without the consent of the author, this paragraph must be reserved and given in a prominent place on the article page

PRML is a classic work in the field of pattern recognition and machine learning, published in 2007. Christpher M. Bishop is a master of Pattern Recognition and machine learning. His 1995 book “Nerual Networks for Pattern Recognition” is also a classic work in Pattern Recognition and artificial neural Networks.

PRML introduces the basic theories and main methods of pattern recognition and machine learning in a simple way, and also covers some of the latest progress in the field of pattern recognition and machine learning. It is not only suitable for beginners to learn, but also has great reference value for professional researchers.

Table of Contents (Chinese version)

  1. An introduction to
  2. A probability distribution
  3. Linear regression model
  4. Linear classification model
  5. The neural network
  6. Nuclear method
  7. The SVM.
  8. Modern graph-based models
  9. The EM algorithm
  10. The approximate inference
  11. The sampling
  12. PCA and some improvements
  13. HMM model and LDS
  14. Integration method

▌PRML Video Learning Materials:

Pattern Recognition and Machine Learning (PRML) by Bishop

  1. The original book
  • Users. Isr. Ist. Utl. Pt / ~ wurmd/Livr…
  1. Corrections:
  • www.microsoft.com/en-us/resea…
  1. The solutions
  • www.microsoft.com/en-us/resea…
  1. Christopher Bishop, Director of Microsoft Research Cambridge
  • www.microsoft.com/en-us/resea…
  1. Part of chapter PPT
  • www.microsoft.com/en-us/resea…
  • www.microsoft.com/en-us/resea…
  • www.microsoft.com/en-us/resea…
  • www.microsoft.com/en-us/resea…

Model Based Machine Learning by Bishop

  1. PRML guru Chris Bishop, director of Microsoft Research Cambridge, and John Winn’s new machine learning book. The most entry-level machine learning book, which starts with practical cases and has few mathematical formulas, is a great introduction to PRML.
  2. www.mbmlbook.com/

code

  1. Matlab
  • prml.github.io/
  1. Python
  • github.com/ctgk/PRML

video

  1. The main reference book of CSCI1420 Machine Learning in Brown University adopts PRML, and the content arrangement is consistent with PRML. There are 23 courses in total.
  • Cs.brown.edu/courses/csc…

PRML notes

  1. Notes on Pattern Recognition and Machine Learning (Bishop) by Jian Xiao
  2. Some Notes on Pattern Recognition and Machine Learning by Tian Yuandong
  • www.yuandong-tian.com/notesOnPRML…
  1. ChillyRain’s “PRML Notes” blog series
  • Chillyrain.is-programmer.com/categories/…
  1. Bishop’s PRML Book: Review and Insights, Chapters 1 — 3
  • Techburst. IO/bishops – also…
  1. PRML reading group
  • www.52nlp.cn/category/pa…
  • www.52nlp.cn/prml Foreword to book club
  • PDF PRML Book Club collection printed version

PRML related materials: third edition, Study notes, Chinese translation, etc

Link: pan.baidu.com/s/1oiZST2Xg… Extraction code: PTHB

The complete code of this book is officially issued with the Matlab version at the following address: prml.github. IO /

CTGK has also released a more commonly used Version of Python on GitHub, which has passed the 2K star mark. The address is github.com/ctgk/PRML

Address: www.6aiq.com/article/158…

This article is copyrighted jointly by the author and AIQ.

If you want to get the full PDF you can get it \

How to obtain: \

1. Follow the Python Meets Machine Learning public account

2. Reply to “PRML” in the background of “Python Meets Machine Learning” public account.

???? Long press the qr code above2Reply "PRML" in seconds to obtain informationCopy the code