2
$\begingroup$

I am a Ph.D student in Electrical Engineering. I am going to study the field of machine learning and I found some textbooks to study this field.

1) Probabilistic Graphical Models: Principles and Techniques by Koller

2) Bayesian Reasoning and Machine Learning by Barber

3) Machine Learning: A Probabilistic Perspective by Murphy

I know statistics a little bit because I studied Bayesian learning for my master degree in EE and I took some math courses such as stochastic processes and probability. But, if a book is too comprehensive and succinct, I have no ability to following the book. I would like to study machine learning in detail in terms of statistical learning. In this case, which book is good for me? Thanks.

$\endgroup$
1
$\begingroup$

I highly recommend Kevin Murphy's book. It covers all major areas of machine learning with sufficient theory and algorithm implementations available at: https://github.com/probml/pmtk3

In addition, I recommend experimenting with scikit-learn library: http://scikit-learn.org/stable/ the user guide covers many important algorithms in machine learning and it's written in Python.

$\endgroup$
0
$\begingroup$

I recommend Introduction to Statistical Learning. I think it hits the sweet spot between theory and practice.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.