[D] Does ML have a "bible"/reference textbook at the Intermediate/Advanced level?

Hello, everyone! This is my first time posting here and I apologise if the question is, perhaps, a bit too basic for this sub-reddit. A bit of an introduction: I am a 23 years old Master's Student enrolled in an Artificial Intelligence programme at a University (which one is irrelevant). Next year I shall have to work on my thesis and the topics that are currently being floated around by my to-be supervisor are: handwriting recognition, historical document analysis, document binarisation, layout analysis, and transcription etc.

I am looking for a book that I can use as a reference throughout my thesis and that I can use in conjunction with research papers and other resources: something like Classical Electrodynamics by John David Jackson for Electromagnetism (if anyone here has a background in Physics) or what Deep Learning by Aaron Couville, Ian Goodfellow, and Yoshua Bengio once was (perhaps still is, I don't know).

My professor, for his courses, typically recommends the following:
- Pattern classification (2nd edition) by Richard O. Duda, Peter E. Hart, David G. Stork (2001), Wiley, New York, ISBN 0-471-05669-3.
- Statistical Pattern Recognition (3rd edition, 2011) by A R Webb, Keith D Copsey, Wiley, New York, ISBN 9781-11995296-1.
- Pattern Recognition and Machine Learning (2006) by Christopher M. Bishop, Springer, ISBN 0-387-31073-8.
- Pattern Recognition (4th edition, 2009) by Sergios Theodoridis, Konstantinos Koutroumbas, Elsevier, ISBN 978-1-59749-272-0.

Would you guys recommend me any of these 4 or perhaps another one that is more state-of-the-art?

Thank you all for the consideration and for the responses in advance! :)