Link to Content:

Created/Published/Taught by:

Christopher Bishop

Content Found Via:

Jiang Li

Free? No

Cost Range:

$24.68 - $94.95

Tags: backpropagation / bayes theorem / bayesian linear regression / bayesian networks / calculus of variations / classification / decision theory / distributions / gaussian distribution / hessian matrix / inference / information theory / k-means / kernel methods / linear regression / logistic regression / markov models / mixture models / multiple linear regression / neural networks / nonparametric statistics / principal component analysis (PCA) / probabilistic models / probability / sampling methods / statistics / Support Vector Machines (SVMs)

Difficulty Rating:

from Springer:

“The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.

This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.

Christopher M. Bishop is Deputy Director of Microsoft Research Cambridge, and holds a Chair in Computer Science at the University of Edinburgh. He is a Fellow of Darwin College Cambridge, a Fellow of the Royal Academy of Engineering, and a Fellow of the Royal Society of Edinburgh. His previous textbook “Neural Networks for Pattern Recognition” has been widely adopted.”

Some sample pages available at Springer.com

Recommended Prerequisites: from the book:

"This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra

is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory."

Go to Content: Pattern Recognition and Machine Learning

## By magsol October 20, 2015 - 12:02 pm

Chris Bishop’s textbook is a dense but informative dive into a solid breadth of basic machine learning topics. His background revolves primarily around neural networks (hence the book’s emphasis in that regard) but has extensive experience in all areas of machine learning, an expertise that comes across in the book.

It’s not the most theoretically-dense machine learning textbook out there, but it’s not going to help a novice implement their first support vector machine either. Be prepared for many “clearly it follows” proofs that, if you are having trouble following, won’t be clear at all. The theory can get pretty dense, but if you can devote time to working through the problems and/or have someone help you getting through the more difficult concepts, it is an excellent resource for developing a core understanding of the basics of machine learning and data science.

Advanced Intermediate- comfortable with data science and prerequisites / has prior experience applying this topicIntermediate - had relevant prerequisites / was familiar with this topic

(3) A Good Amount

Detailed Content Survey:Required for school or in order to keep or be eligible for a job

This was my first experience with this publisher/series

Yes

Most (i.e. read the entire book but didn't complete the exercises)

I had other priorities or time constraints / stopping was unrelated to the content

A thorough understanding of linear algebra and high-dimensional geometry will help immensely for getting through the theoretical proofs that are included.

"Clearly it follows that..."

The first several chapters are excellent, though it will take a few re-reads before the lessons really start to sink in. Only once you're comfortable with the content in the first half of the book should you dive into the second half.

Read the first few chapters (part of a course), went back and worked the problems, re-read the chapters, then moved into the second half of the book.

An entire semester, plus several weeks thereafter to truly absorb the content.

A beginner in ML with a good math background

## By rgap October 21, 2015 - 4:30 pm

This book is awesome and could be much better if it has like already-implemented lessons written in python, I know some guys who tried to do it like in https://github.com/jamt9000/prml that should be very useful for students learning from this book :/

Advanced Intermediate- comfortable with data science and prerequisites / has prior experience applying this topicIntermediate - had relevant prerequisites / was familiar with this topic

(5) More than I even wanted to!

No ratings yet.

## By expectopatronum October 29, 2015 - 2:43 pm

The book covers many interesting topics that you will meet in different machine learning / data science lectures. I especially like Linear Models and the EM algorithm. It also contains topics I’ve never heard before (e.g. Sparse Kernel Machines). I found the appendices A (data sets) and B (probability distributions) especially useful.

It also contains many, many exercises for each chapter, so it can be a useful companion for your study and also during your job.

Advanced Intermediate- comfortable with data science and prerequisites / has prior experience applying this topicIntermediate - had relevant prerequisites / was familiar with this topic

(4) A Lot

Detailed Content Survey:Needed to gain or strengthen a specific skill or understand a specific topic

This was my first experience with this publisher/series

Yes

Very little

I had other priorities or time constraints / stopping was unrelated to the content

## By trianglegirl May 2, 2017 - 4:17 am

A thorough introduction to machine learning techniques to a technical level, well laid out and with plenty of intuition.

I frequently refer to this book, especially when introducing clustering and classification methods to students.

Advanced Intermediate- comfortable with data science and prerequisites / has prior experience applying this topicAdvanced - had practical experience with this topic beyond intermediate level / could teach others this or related topics

(3) A Good Amount

No ratings yet.