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I'm taking an introductory graduate course in statistical programming. I usually like to read textbooks along with my coursework, but the professor doesn't have any suggestions for books matching the syllabus. Is there any book that could help me read up on this material?

The basic outline of the class is this: For each topic, the professor demonstrates the underlying math on the blackboard, then writes code to perform the algorithm. He moves very quickly, so I'm hoping there might be a book (or several) I could supplement my notes with. I'm interested in both the math and the algorithms.

Here is the syllabus topics list:

The purpose of this course is to teach the art of statistical programming in R, Python, and C/C++, by writing computer code to implement the following core algorithms in statistical computing.

. Least squares regression, sweep operator, QR decomposition

· Eigen computation, Principal Component Analysis

· Logistic regression, Newton-Raphson

· Lasso, coordinate descent, boosting, solution path

· Feed-forward neural network, back-propagation

· EM algorithm, Gaussian mixture, factor analysis

· Random number generators, Monte Carlo integration

· Metropolis algorithm, Gibbs sampling, Bayesian posterior sampling

When going through the above topics, the focus will be on algorithms and especially programming, instead of theories of learning, inference and computing.

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Numerical Analysis for Statisticians, by Kenneth Lange, is a wonderful book for this purpose. It provides most of the necessary background in calculus and some algebra to conduct rigorous numerical analyses of statistical problems. This includes expansions, eigen-analysis, optimisation, integration, approximation theory, and simulation, in less than 600 pages.

Note: I reviewed this book for CHANCE a few years ago, if you want to read the whole review.

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