Probability theory is the mathematical study of uncertainty. It plays a central role in machine learning, as the design of learning algorithms often relies on probabilistic assumption of the data. Now, are you searching for some good books in Probability to read? Here is our list.

1. A Course in Probability Theory by Kai Lai Chung

This book assumes that you have a certain degree of mathematical maturity, but gives you very thorough proofs of the basic concepts of rigorous probability.

2. An Introduction to Probability Theory and Its Applications by William Feller

This is a two volume book and the first volume is what will likely interest a beginner because it covers discrete probability. The book tends to treat probability as a theory on its own.

3. Bundle of Algorithms in Java, Third Edition, Parts 1-5: Fundamentals, Data Structures, Sorting, Searching, and Graph Algorithms by Robert Sedgewick

An excellent resource (students, engineers and even entrepreneurs) if you are looking for some code that you can take and implement directly on the job.

4. Data Mining: Practical Machine Learning Tools and Techniques by Ian H. Witten

This one is a must have if you want to learn machine learning. The book is beautifully written and ideal for the engineer/student who doesn’t want to get too much into the details of a machine learned approach but wants a working knowledge of it.

5. Discovering Statistics Using R by Andy Field

This is a good book if you are new to statistics & probability while simultaneously getting started with a programming language. The book supports R and is written in a casual humorous way making it an easy read.

6. Fifty Challenging Problems in Probability with Solutions by Frederick Mosteller

This book is a great compilation that covers quite a bit of puzzles. What I like about these puzzles are that they are all tractable and don’t require too much advanced mathematics to solve.

7. First Course in Probability by Sheldon Ross

This introduction presents the mathematical theory of probability for readers in the fields of engineering and the sciences who possess knowledge of elementary calculus. Presents new examples and exercises throughout. Offers a new section that presents an elegant way of computing the moments of random variables defined as the number of events that occur.

8. Introduction to Algorithms by Thomas H. Cormen

The book covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers. Each chapter is relatively self-contained and can be used as a unit of study.

9. Introduction to Probability by Dimitri P. Bertsekas

If you want to learn probability outside of a physical classroom, this book is an excellent choice. It does not require prior knowledge of other areas, but the book is a bit low on worked out examples.

10. Introduction to Probability Theory by Paul G. Hoel

This book is an excellent choice for anyone who is interested in learning the elementary probability theory (i.e. calculus-based probability rather than measure theoretic probability). The book assumes the readers have no prior exposure to this subject.

11. Probability and Statistics by Morris H. DeGroot

This is an outstanding book for those with a strong math background. It covers everything that one would learn in a one-year statistics course and more, including lots of sections on Bayesian methods.

12. Probability Theory: A Concise Course (Dover Books on Mathematics) by Y.A. Rozanov

This book is not for everyone, as it does require a small degree of mathematical sophistication. But it will prove most useful for a very large audience. For serious beginning mathematics and science students it will provide the quickest way to learn the subject.

13. Probability Theory: The Logic of Science by E.T. Jaynes

Going beyond the conventional mathematics of probability theory, this book views the subject in a wider context. It discusses new results, along with applications of probability theory to a variety of problems.

14. The Probability Tutoring Book: An Intuitive Course for Engineers and Scientists (and Everyone Else!) by Carol Ash

It is a self-study guide for practicing engineers, scientists, and students, this book offers practical, worked-out examples on continuous and discrete probability for problem-solving courses. It is filled with handy diagrams, examples, and solutions that greatly aid in the comprehension of a variety of probability problems.

15. Understanding Probability: Chance Rules in Everyday Life by Henk Tijms

This is a great book to own. The second half of the book may require some knowledge of calculus. It appears to be the right mix for someone who wants to learn but doesn’t want to be scared with the “lemmas”.

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