I am currently studying the Elements of Statistical Learning book. The following equation is in page 120. It calculates the Hessian matrix for the log-likelihood function as follows \begin{equation} \dfrac{\partial^2 \ell(\beta)}{\partial\beta\partial\beta^T} = -\sum_{i=1}^{N}{x_ix_i^Tp(x_i;\beta)(1-p(x_i;\beta))} \end{equation} But is the following calculation it is only calculating $\dfrac{\partial^2\ell(\beta)}{\partial\beta_i^2}$ terms. But Hessian matrix should also contain $\dfrac{\partial^2\ell(\beta)}{\partial\beta_i\partial\beta_j}$ where $i\neq j$. Please explain the reason for missing out these terms.

I'm a Computer Science engineer with no background in statistics or advanced math. I'm studying the book Python Machine Learning by Raschka and Mirjalili, but when I tried to understand the math of the Machine Learning, I wasn't able to understand the great book that a friend suggest me The Elements of Statistical Learning. Do you know any easier statistics and math books for Machine Learning? If you don't, how should I move?

I am working through Elements of Statistical Learning, and unfortunately have found great difficulty in following the math. I have taken the standard series of Calculus courses (i.e., up to multi-variable calculus) and linear algebra, and have taken introductory (non-calculus-based) statistics and econometric courses. Please let me know if there are any courses or textbooks you would recommend in order to get my mathematics/statistics knowledge up to speed. For example, I am considering working through the relevant portions of Statistical …

It seems to be generally acknowledged that decision trees have low prediction accuracy. Is there a concise explanation for why they have low accuracy? I've read this so much, I've accepted it to be true, but I realize I don't have any intuition as to why it's true. As an example, here's an excerpt from Elements of Statistical Learning (page 352): Trees have one aspect that prevents them from being the ideal tool for predictive learning, namely inaccuracy. They seldom …