For a quick introduction to SVMs (and a wide range of machine learning algorithms) then I highly suggest Andrew Moore's tutorials. They're extremely concise and

For a quick introduction to SVMs (and a wide range of machine learning algorithms) then I highly suggest Andrew Moore's tutorials. They're extremely concise and well described, taking the format of a university lecture. Only the PDF slides are available however. http://www.autonlab.org/tutorials/svm.html http://www.autonlab.org/tutorials/svm.html If you want a good introduction which actually derives the math and logic behind SVMs then I'd suggest looking at Stanford's AI/ML video lectures available for free here - http://see.stanford.edu/see/lecturelist.aspx?coll=348ca38a-3a6d-4052-937d-cb017338d7b1 http://see.stanford.edu/see/lecturelist.aspx?coll=348ca38a-3... It begins with the first few lectures which covers introductory knowledge and some other machine learning algorithms but lectures 6-8 cover the theory and principles behind SVM. The great thing about this is that relatively little knowledge is assumed on the student's part and he provides a great deal of notes and handouts on any areas the students may be fuzzy. Unless you're going to be merely using a prebuilt machine learning library I feel that understanding the math and logic behind the algorithms is vital.