Data Science and Machine Learning: Mathematical and Statistical Methods (Chapman & Hall/CRC Machine Learning & Pattern Recognition)

I love the book, especially on the section of introducing Bayesian learning.

Very helpful

I really enjoyed working through this book. It is definitely mathematical and algorithmic in its treatment of the topics covered: Statistical "Learning", Monte Carlo Methods, Unsupervised "Learning", Regression Models, Regularization and Kernel Methods, Classification, Decision Trees and Ensemble Methods, Deep "Learning" ( Neural Networks ) As a statistician and data scientist, I find the convoluted "machine learning" terminology very affected and as helpful as trying to design a plane based on the flying dynamics of a bird. Sometimes the rigourous mathematical notation is difficult to follow on the initial reading. Most algorithms are implemented in Python but the code should be more clearly documented so that one can follow the implementation of the solution without getting stuck on coding issues as the book encourages the reader to focus on the algorithm and to not treat the python code as a black box. The list of references is quite complete and it was interesting to check my library to see just how many of the references I already had. If this book is to be used for training analysts then there should be more practical examples and code solutions available

The data is the fuel of the new industry of the future, and this new science based in statistic and mathematical modeling have a deep background that must be learnt to understand the gist this new technology and theirs applications. This year I have gotten a certificate in Machine Learning in the MIT and of course I studied from several excellent books, but just one cover all the fundamental knowledge in a clear, rigorous and elegant way. Even with phyton programming to test the algorithms and stay in touch in a real way with the mathematical technics required to learn in a professional way. The book have other important advantage, the format is big, clean and full of colour, more when one must understand an specific notation in a rigorous way. A great book really thought in the students who want to progress in this subject. Ulyses of James Joyce is to classical literature as this book is to Data Science and Machine Learning. An splendid job of the professor Dirk P. Kroese and his colleagues. Marcelo Cortés CHILE.

I'm very early on in the text, but the text is impressive in the breadth and depth of its coverage, along with its attention to the mathematical theory. The exercises are challenging, which makes the text a little tricky for self-study -- at least to the extent your self-study is enhanced by knowing if you got the problems right or wrong. If I ever make it through the whole text, and change my opinion, I'll come back and edit this review; but I think just getting through Chapter 2 will be a semester's worth of knowledge.

Excellent book. It was delivered in perfect conditions. Great acquisition

A great book for all , conceppts are clear and well explained.

Despite the hard-copy needing an entire tabletop to read through, I still loved the content and recommend this book to those wanting to get a taste of both theory and practice. It does cover a vast array of topics, from Monte Carlo to regularization and more, and is very nicely written. It has just the right amount of detail with examples, so that it does not waste the reader's time with information that can easily be looked up elsewhere - if at all necessary.