Recommend good books for a beginner to learn about Support Vector Machines (SVM) I am doing a semester project on Support Vector Machines, where I am supposed to read up on the mathematics behind it, as well as give some proofs of the mathematics. 
I am an undergraduate, with a foundation of linear algebra and optimization. 
However, I cannot find any textbooks that teaches support vector machines in details...
Can I get some recommendations?
Video recommendations are welcome too, but ultimately, I need a strong working foundation of SVMs enough for me to write a 25 page report.
 A: I've found that Christopher Bishop's book on pattern recognition and machine learning has been a very nice text. Andrew Ng also has some great video lectures on machine learning and SVMs if you have a Google.
A: Since you have a background in optimization, I recommend reading the explanation of Support Vector Machines that appears in Chapter 8 of Boyd and Vandenberghe (free online).
The book Linear and Nonlinear Optimization, by Griva, has several pages about Support Vector Machines.
I've also heard good things about the book Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, by Scholkopf and Smola.
A: There is a book named $\textbf{An Introduction to Support Vector Machines and Other Kernel-based Learning Methods}$ and is dedicated entirely to SVM covering both theoretical as well as numerical aspects of SVM.
http://www.amazon.com/Introduction-Support-Machines-Kernel-based-Learning/dp/0521780195
A: Can't believe no one mentioned Alexandre Kowalczyk's book, which is free: https://www.svm-tutorial.com/2017/10/support-vector-machines-succinctly-released/ (that site has many blog posts, the book can be found here, now: https://www.syncfusion.com/succinctly-free-ebooks/support-vector-machines-succinctly)
I've found it to explain the math well.
