- [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
----------- links:
- [0] [PDF](http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%...)
- [0][AMZ](https://www.amazon.com/Pattern-Recognition-Learning-Informat...)
- [2] [amz](https://www.amazon.com/Reinforcement-Learning-Introduction-A...)
- [2] [site](https://www.deeplearningbook.org/)
- [3] [amz](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Ma...)
- [3] [pdf](http://incompleteideas.net/book/bookdraft2017nov5.pdf)
- [4] [amz](https://www.amazon.com/Language-Processing-Synthesis-Lecture...)
- [5] [amz](https://www.amazon.com/Introduction-Linear-Algebra-Gilbert-S...)
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...