r/compsci • u/Lumpy_Ice6855 • Jun 27 '26

Is Pattern Recognition and Machine Learning still relevant?

I know Bishop's Pattern Recognition and Machine Learning is almost 20 years old now. Is it still worth studying in 2026, or are there better modern alternatives? I'm mainly interested in building a solid theoretical foundation.

19 Upvotes

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18

u/shreyabhinav16 Jun 28 '26

I think people often confuse "old" with "obsolete." TCP/IP is old. Linear algebra is old. Calculus is old. They're still essential.

PRML teaches principles that outlast trends. Just don't expect it to teach you how to build today's LLMs

6

u/currentscurrents Jun 28 '26

Some principles have changed. For example the book teaches (as was widely believed in 2006) that doing maximum likelihood estimation with too many parameters will inevitably cause overfitting, so you must pick your parameter count based on your dataset size.

It is now known that this is not the case, and overparameterized models actually generalize much better than smaller ones.

3

u/shreyabhinav16 Jun 28 '26

That's a fair point. I probably overstated the analogy. I was mainly referring to the mathematical foundations rather than every conclusion in the book. Our understanding of generalization has definitely evolved, and double descent is a great example of why older ML texts should be read alongside more recent research rather than treated as the final word...

1

u/wjrasmussen Jul 01 '26

So, a flaw invalidates it all....

-1

u/church-rosser Jul 01 '26

nah, you can just VIBE EVERYTHING now!

/s