I'm trying to get a solid knowledge of linear algebra for statistics and machine learning. I didn't study math during college/university. I have the very basic knowledge of what is a dot product, a matrix inverse, and a transpose, but I tend to stumble on concepts like rank of a matrix. There are several books out there to learn linear algebra (to name just a couple):
- Linear Algebra (Dover Books on Mathematics) by Shilov
- Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares by Boyd
However, whichever course of stat / math you take starts always with the teacher saying: "Only listening to my class is useless if you don't do the exercises". Alright, but if you are studying on your own, you also need the answers (being confident that your answer to an exercise is the right one when you are actually flatly wrong is astonishingly easy according to my own experience…). The above mentioned books have exercises but don't provide the answers. Could somebody recommend a source of exercises with solutions with the final goal of being able to understand (at least a little better) a reference book of machine learning like The Elements of Statistical Learning by Hastie and Tibshirani?
Best Answer
Give Linear Algebra Problem Book by Paul Halmos a try. It is written in a conversational style and has both hints and solutions at the back of the book.
Another book that has answers to exercises is Jim Hefferon's Linear Algebra, which is freely available online, but I personally have not read it.