# Introduction to Linear Algebra, Fifth Edition

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The professor who wrote this is Jean Gallier, and I had him for advanced linear algebra at Penn. I am also pretty close to him in so far as a student can be close to a professor. On a personal note, he is one of the funniest professor I've had, and all math professors are characters.

For the people who are interested in ML, the thing to remember here is that he is a Serious mathematician, and he values rigor and in-depth understanding above all. A lot of his three star homework problems were basically impossible. He writes books first and foremost so he can understand things better. In math books, there's the book you first read when you don't understand something, then the book you read when you understand everything. This is book in the link.

for linear algebra, this:https://www.amazon.com/Introduction-Linear-Algebra-Gilbert-S...)

Execution of a determinant. [0]

Always love it when a professor can bring in some comic relief in the midst of a very heavy math topic. The students seem to enjoy it. I am self-teaching myself background math for preparing me to the likes of PRML-Bishop, and I wholeheartedly recommend his Linear algebra course available on MIT Courseware[1] coupled with his book[2]

[0] https://www.youtube.com/watch?v=amv58LCqCMI [1] https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra... [2] https://www.amazon.com/Introduction-Linear-Algebra-Gilbert-S...

In machine learning, hands down these are some of the best related textbooks:

- [0] Pattern Recognition and Machine Learning (Information Science and Statistics)

and also:

- [1] The Elements of Statistical Learning

- [2] Reinforcement Learning: An Introduction by Barto and Sutton

- [3] The Deep Learning by Aaron Courville, Ian Goodfellow, and Yoshua Bengio

- [4] Neural Network Methods for Natural Language Processing (Synthesis Lectures on Human Language Technologies) by Yoav Goldberg

Then some math tid-bits:

[5] Introduction to Linear Algebra by Strang