Download Ebook Machine Learning: A Bayesian and Optimization Perspective (.Net Developers) 1st edition
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Machine Learning: A Bayesian and Optimization Perspective (.Net Developers) 1st edition
Download Ebook Machine Learning: A Bayesian and Optimization Perspective (.Net Developers) 1st edition
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- Published on: 1709
- Binding: Hardcover
Most helpful customer reviews
12 of 13 people found the following review helpful.
It is a great book!
By Paulo S. R. Diniz
It is a great book!!! It covers a wide range of subjects related to machine leaning not found in other books. It is well written and includes detailed reference list in each subject matter. The book should be useful for practitioners, graduate students and academics. I am glad I bought it.
7 of 8 people found the following review helpful.
Great Book!!! A Machine Learning must....
By Andres Mendez
As a practitioner of Machine Learning, I am so amassed about Theodoridis' abilities to deliver fresh and precise content about the so fast evolving field of Machine Learning. This book is a must on the shelves of anybody calling herself or himself a data scientist. Sections like the ones about sparse data, Learning Kernels, Bayesian Non-Parametric Models, Probabilistic Graphical Models and Deep Learning make of this book a forefront reference on a field that is transforming the world.
20 of 24 people found the following review helpful.
Good but may be hard to follow
By Andrei
I'm still looking for a "perfect machine learning theory book": the one which is a pleasure to read and that covers most of concepts you see here and there all the time but always wanted to know how exactly they work: log-linear, maximum likelihood, MAP, least squares and MLS, expectation maximization, stochastic gradient descent, CRFs, mixtures of gaussian, and many others. I would like that the book explain to me why should I use this model or algorithm, why previous one would not be good? And I would like that the author take the time to carefully guide the reader throughout the theory, without leaving him alone with a bunch of matrix equations or integrals like if they were evident.
I'm not a novice in the AI: I have a PhD (not in the theoretical Machine Learning though) and several years of practical experience with the algorithms. But most of the time I use the algorithms and models like blackboxes. My goal, however, is not only be able to use the algorithms and know where and how each algorithm can be used, but really understand the math that drives each them.
Unfortunately, this is not the book that can help me with my goal. In the beginning of each chapter the author really tries to move slowly with a care to details, but very fast the math becomes the only language used on the page. If, in the middle of a section you didn't understand how equation 12 follows from equation 11, your only option is to skip the remainder of the section and this is very frustrating.
As an example, when presenting the "central limit theorem", the author writes "Consider N mutually independent random variables, each following its own distribution with mean values ... and variances ... Define a new random variable **as their sum**: ... Then the mean and variance of the new variable are given by...". Here, or before, no definition of a **sum of two random variables** was presented. But this is very important to understand, because later, for example, in the "Linear Regression" section of Chapter 3, the author writes "If we model the system as a linear combiner, the dependence relationship is written as: " (a linear combination of several random variables follows). What does this mean: a linear combination of ***random variables***? How is this related to the central limit theorem which says that by adding up several random variables, the resulting variable tends to have a gaussian distribution? Author, please don't hurry up, it's a book, not a NIPS paper!
Furthermore, the whole section "3.10.1 LINEAR REGRESSION: THE NONWHITE GAUSSIAN NOISE CASE" on page 84 cannot be directly understood from the text because the author does not explain how the joint log-likelihood function L(theta) for the model of y dependent on theta, x and nu can be constructed. The equation 3.57 gives the final expression for L(theta) but no clues on how to build it if we only have a linear model for y. I spent the whole evening just to understand that to build the joint log-likelihood function one has to transform the y = theta*x + nu into the expression p(y=yn | x=xn, theta, nu) and in order to obtain one such expression for each yn, one has to write p(y=yn | x=xn, theta, nu) = sum_k p(xn*theta=k)p(nu=yn - xn*theta). Then, the joint log likelihood L(theta) can be obtained as ln p(y=y1 | x=x1, theta, nu) + ln p(y=y2 | x=x2, theta, nu) + ... + ln p(y=yn | x=xn, theta, nu).
The internet is full of information on the subject of machine learning. Almost every subject is already explained by multiple sources. The problem with the information of the Web is that it is dispersed and often incomplete. If one decides to write a book on this subject, it has to be complete and self-contained. With this book, unfortunately, one still has to google, decrypt and guess things just too often to call the reading process a pleasure.
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