Book · 2006
Pattern Recognition and Machine Learning
by Christopher M. Bishop
Chris Bishop opens with Bayes' theorem and doesn't let go until three hundred pages of matrices later you understand why a neural network is really just curve-fitting with priors.
What does Reddit think of Pattern Recognition and Machine Learning?
Chris Bishop's 2006 Springer text shows up as a citation more than an argument. r/datascience answers a 'what should I read' thread with two lines: 'Chris Bishop, Pattern Recognition and Machine Learning,' at ↑47, and moves on with no elaboration needed. r/compsci files it next to Boyd's Convex Optimization and Norvig and Russell's AI text in a personal-favorites round-up, the kind of company that assumes you already know why it belongs there. r/MachineLearning calls it 'classic and still relevant' at ↑36, nearly two decades after publication, and the same subreddit also lists Bishop's newer Deep Learning: Foundations and Concepts right alongside it, tagged only as 'the same author as the previous.' Nobody in the displayed excerpts argues against this book. Nobody really has to. It rarely turns up as anyone's first pick in these threads. It turns up once someone already knows the question they're asking.
Community feedback & reader fit
Themes
- · Bayesian foundations of machine learning
- · graduate-level math prerequisite
- · canonical status on ML reading lists
- · Bishop's PRML-to-Deep-Learning pipeline
- · reference text versus intro text
Common praise
- + r/compsci puts it on the shelf next to Boyd's Convex Optimization and Norvig and Russell's AI text without needing to defend the choice.
- + r/datascience answers a whole reading-list question with just the title and author, no follow-up questions asked.
- + r/MachineLearning still calls it 'classic and still relevant' close to twenty years after Springer printed it.
Common criticism
- − None of the displayed threads explain what makes it hard going; the recommendations assume you already know why the math matters.
- − The same r/MachineLearning post that recommends it also surfaces Bishop's newer Deep Learning: Foundations and Concepts, so the thread reads as an upgrade note more than a case for buying this one specifically.
- − It never shows up as the first book anyone names when the question is how to start; it shows up after harder framing already happened.
Who it's for
Come to this one with a stats or linear algebra background already in hand, because r/compsci and r/MachineLearning both file it as a reference next to Boyd and Norvig-Russell, not as a first exposure to the field. If a Coursera-style intro already covered gradient descent and you want the probabilistic reasoning underneath it, this is where r/datascience and r/MachineLearning point without hesitation. Newer readers weighing this against Bishop's own Deep Learning: Foundations and Concepts should treat the two as companions, not as a choice between them; the displayed threads never frame them as competitors.
Mentions over time
Top subreddits
Which Reddit comments matter for Pattern Recognition and Machine Learning?
The most relevant excerpts across the subreddits where this book is mentioned — opinionated, argued takes first, then top-upvoted mentions. Click through to read the full thread.
“With your math and CS background you're in a good spot. Start with Russell and Norvig's Artificial Intelligence: A Modern Approach. It's the standard textbook most programs use and covers everything from search algorithms to neural networks with actual theory, not hand-waving. For machine learning s…
“There is a lot of promising theory, but as you've said, typical introductory resources are geared towards students much less interested in the math The theoretical groundwork for machine learning is statistical learning theory, typically PAC or Bayesian stats. There are well established resources o…
“Some personal favorites. Continuous optimization theory: * Nonlinear Programming - Bertsekas * Convex Optimization - Boyd Artificial Intelligence: * Artificial Intelligence: A Modern Approach - Norvig and Russel Machine learning: * Pattern Recognition and Machine Learning - Bishop Machine lea…
“Chris Bishop, Pattern Recognition and Machine Learning
“I say this according to my several years of experience in advising new comers to the ML field: 1. If you don't have much time: start with ISL (you may want to wait for the upcoming Python edition). Should take about a month if you read everyday and code at the same time. This book is very accessibl…
“I could recommend you three options: - Deep Learning: Foundations and Concepts: - Pattern Recognition and Machine Learning: (the same author as the previous) -
“Bishop, Christopher M. Pattern recognition and machine learning. Springer, 2006. Classic and still relevant
“After completing my BSc. in EEE, for the love of python, I stepped into the world of data science and machine learning. Here is how my reading and research habit have evolved over time. In the beginning, I had research on my mind and started with basic statistical methods. To build a deeper intui…
“Having done an MEng at Oxford where I dabbled in ML, the 3 key texts that came up as references in a lot of lectures were these: Pattern Recognition and Machine Learning (Information Science and Statistics) (Information Science and Statistics)
“I'm a huge fan of Christopher M. Bishop's [Pattern Recognition and Machine Learning](
“I work in misclassification and outlier detection, and lately also zero-shot classification. Bishop‘s Pattern recognition and machine learning, an Tibshirani‘s Elements of statistical learning are the two book that I learned the most from. For any cutting edge stuff, including transformer architec…
“If you want traditional ML advanced topics (i.e. other than deep learning) I would recommend "Pattern Recognition and Machine Learning" by Christopher Bishop. It covers in-depth theory behind many ML algorithms and techniques. Also you may want to check "The Elements of Statistical Learning". For d…
“Pattern Recognition and Machine Learning by Bishop if you need a brush up on the basics and solid mathematical formulation (his way of writing math became the somewhat standard used by most)
“My thoughts on the common machine learning books * Deep learning (DL); Goodfellow, Courville, Bengio: Covers deep learning from statistical / connnectionist perspective. * Pattern recognition and Machine Learning (PRML); Bishop: accessible introduction to machine learning. Bishop uses more exa…
“Pattern recognition and machine learning, by Bishop, is one of the classics. You can easily find the PDF online, e.g. here. In general, if you get familiar w…
Convinced? Pick up Pattern Recognition and Machine Learning
What else does r/MachineLearning read?
Other books mentioned in the same sub, ranked. Shared-sub overlap with this title breaks ties.
Pattern Recognition and Machine Learning — frequently asked
Is Pattern Recognition and Machine Learning still relevant in 2026?+
Yes, per r/MachineLearning's own framing: a mention calls it 'classic and still relevant' at ↑36, nearly two decades after Springer published it. That's one thread's judgment, not a fleet consensus, but nothing in the displayed excerpts contradicts it, and r/datascience answers a reading-list question with just the title and author at ↑47.
Is Pattern Recognition and Machine Learning a good first ML book?+
Probably not your first. The displayed threads file it as a reference alongside Boyd's Convex Optimization and Norvig and Russell's AI text in r/compsci, company that assumes prior math. r/learnprogramming's top-upvoted answer to a beginner (↑77) points to Russell and Norvig's AI: A Modern Approach instead, not to this book.
Should I read Pattern Recognition and Machine Learning or Bishop's newer Deep Learning book?+
Depends on what you already have. r/MachineLearning's ↑43 thread lists both as options from 'the same author,' with no ranking between them. Nothing in the displayed excerpts argues one replaces the other, so read this one for the probabilistic foundations, and the newer book for how those ideas map onto current deep learning practice.
What does Reddit actually think of Pattern Recognition and Machine Learning?+
Mostly that it's the answer once you already know the question. r/datascience cites it with two lines and no elaboration at ↑47; r/compsci lists it among 'personal favorites' at ↑51. None of the displayed excerpts argue against it, but none argue passionately for it either. It's treated as settled, not debated.