Information Theory, Inference, and Learning Algorithms by Shekh Hoque | (PDF) Free Download

Description

This book is aimed at senior undergraduates and graduate students in Engineering, Science, Mathematics, and Computing. It expects familiarity with calculus, probability theory, and linear algebra as taught in a first- or secondyear undergraduate course on mathematics for scientists and engineers. Conventional courses on information theory cover not only the beautiful theoretical ideas of Shannon, but also practical solutions to communication problems. This book goes further, bringing in Bayesian data modelling, Monte Carlo methods, variational methods, clustering algorithms, and neural networks. Why unify information theory and machine learning? Because they are two sides of the same coin. In the 1960s, a single field, cybernetics, was populated by information theorists, computer scientists, and neuroscientists, all studying common problems. Information theory and machine learning still belong together. Brains are the ultimate compression and communication systems. And the state-of-the-art algorithms for both data compression and error-correcting codes use the same tools as machine learning.

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐Read the book is like talking to a teacher. I can feel the soul of the author. (He had passed away). The book contains solutions to selected problems that are convenient to me for self-study.

⭐As a grad student in optimization with a background in physics, I really enjoy the multi-disciplinary approach of this book. Connections between different fields are frequent throughout the book. However, I often am frustrated with the book’s style. Often, something that needs further explanation or clarification does not receive it, and I am forced to “google” the explanation that should be there but isn’t.

⭐Highly recommended. Very coherent and readable. Unique angle of view. The author didn’t try to scare the reader away like a lot of other authors did.

⭐The hardcover is much better than the soft cover. Mackay was a visionary, can’t wait to read the book.

⭐This is a really good book. It serves as a good introduction to Information theory but it has enough depth and cover enough material be to interesting and insightful even to someone who has already studies the subject in depth. This book is fairly high level and though I found it very interesting and insightful it does not have enough practical information to be useful (on its own) for solving problems in information theory or writing learning algorithms.

⭐MacKay is the pioneer in the field of machine learning theory. I recommend it to people who have good physics sense and want to learn the basic idea of learning theory.

⭐Other reviewers have provided all the details you need to know before buying.Just to chime in that this is one of the best technical books I have ever read.It brims with insight and beautiful illustrations of ideas both old and novel.Although you can find a free copy online, do consider getting the print version.It is a great tome to have, and Dr. MacKay certainly deserves the royalties.

⭐If someone looking for a different perspective, interesting and challenging, this is a book to read.

⭐A delightful tour of information theory and inference. I’m about half-way the book now, and every page has been a thrill.The author passed away too soon. But he leaves a lot behind in this book.

⭐A brilliant book written by someone who seems to have been a brilliant educator. I love the fact that you can read the whole book online before purchase – I found the online version excellent but wanted a hard copy… that is how useful this book is!

⭐An interesting read, well written and you can download the PDF for free but having the dead tree version as well to read in the bath is sooooo much better.

⭐A book that rewards effort. It combines many fascinating topics at the heart of modern technology, and does so with clarity and wit.

⭐This is unique among the books I have encountered on information theory at this level, indeed one of the most reader-friendly accounts of any mathematically complex topic that I have ever read. The style makes the (difficult) subject matter very accessible. There are plenty of illustrations, which really do help with understanding, as well as examples with (mostly) answers provided, which are also valuable. The provision of answers to examples is frowned upon by purists, who say readers should just work them out for themselves, but we can’t always succeed with every one, and I personally hate to be hung up on an example that I can’t do.To appreciate the benefits of Mackay’s approach, compare this book with the classic ‘Elements of Information Theory’ by Cover and Thomas. That book was first published in 1990, and the approach is far more ‘classical’ than Mackay. It is certainly less suitable for self-study than Mackay’s book. That said, I find Cover and Thomas very useful for providing the formal mathematical proofs of the theorems. After reading Mackay and understanding a topic, I then read Cover and Thomas on the same area and find the formal exposition of it, which complements Mackay nicely. I would not be without either book.PS: I have subsequently discovered an excellent series of lectures by the author available online, essentially covering the main topics of the book. The lectures clarify the rather dense presentation in the book, and I have found them invaluable. They can be found by Googling “Mackay information theory lectures”.

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