Machine learning is taught badly in one specific way that this book fixes. Most courses present a sequence of algorithms, each with its own logic, and the student ends up with a toolbox and no theory of what the tools have in common. Bishop's answer is that they are all doing inference under uncertainty, and that once you see everything through probability the apparent variety collapses into a small number of ideas applied repeatedly. Working through this book is the most reliable way I know to acquire that perspective, and it changes how you think about the field permanently.
The opening chapters set up probability theory, decision theory and information theory, then linear models for regression and classification are developed from a Bayesian starting point rather than a least squares one. That reframing is the point. You see why regularisation is a prior, what model comparison actually means when you are integrating rather than optimising, and why the frequentist procedures you learned elsewhere are approximations to something more general. From there it goes into neural networks as they stood in 2006, kernel methods and Gaussian processes, sparse kernel machines, graphical models, mixture models and expectation maximisation, approximate inference including variational methods, sampling methods, continuous latent variables, sequential data and hidden Markov models, and combining models.
The graphical models material is a highlight and remains hard to find treated this well elsewhere. Directed and undirected models, conditional independence, inference in trees and factor graphs, the sum product and max sum algorithms, all developed carefully with the diagrams doing real work. Anybody who has tried to learn this from papers knows how much easier it is when somebody has laid out the whole framework first. Similarly the chapters on variational inference and on sampling are the clearest introductions I have encountered to material that is otherwise scattered across a research literature that assumes you already understand it.
The production quality is worth noting because it is rare. The figures are carefully constructed and each one is making a specific point. The notation is consistent across seven hundred pages, which sounds trivial until you have read a book where it is not. The exercises are graded by difficulty and solutions exist, which makes independent study genuinely possible rather than nominally possible.
Microsoft Research making the PDF freely available was a good thing for the field, particularly given that the print book has spent years being scarce and absurdly priced on the used market. Now the elephant, and it is a large one. This book was published in 2006. Deep learning as we now understand it does not appear, because it did not exist.
The neural network chapter describes multilayer perceptrons trained by back propagation, which is correct and foundational and gives you nothing about convolutional architectures, attention, transformers, or any of the practice that makes modern networks work. The emphasis on kernel methods and Gaussian processes reflects where the research energy was at the time, and while that material is far from useless, it is not where the field went. If you read only this you would have a distorted picture of contemporary machine learning. Bishop himself resolved this.
His 2023 book, Deep Learning: Foundations and Concepts, written with Hugh Bishop, applies the same pedagogical care to the modern material and is free to read on its own site. If your interest is neural networks, go there directly. Read this one if you want the probabilistic and Bayesian foundations, which the newer book covers more briefly because it assumes you can get them elsewhere. The two together are a strong pairing.
The mathematical demands are also real. You need linear algebra you can actually use, comfort with multivariate probability, and enough calculus to follow a derivation involving integrals over parameters without losing the thread. This is not a book you can skim. Every page assumes the previous one landed.
There is also nothing practical in it. No code, no implementation guidance, no discussion of how any of this behaves on real data at scale. That is a legitimate scope decision for a theory text, but it means this cannot be your only resource unless you intend to remain theoretical. My 4.1 reflects a book that is outstanding at what it does and is now covering a specific slice of the field rather than the field.
The probabilistic perspective it teaches has not aged at all and is arguably more valuable now than when it was written, given how much modern practice involves reasoning about uncertainty in systems that do not expose it. What has aged is the assumption that kernel methods and graphical models were where things were heading. Read it for the way of thinking, not for the catalogue of methods.