AI: A Modern Approach devotes most of its considerable bulk to symbolic AI not because there was no machine learning "back then" (2003, last edition I saw) but because for most of the hisotry of AI most of the work has been on symbolic AI and statistical machine learning was a small sub-field, sometimes not even placed under AI (for example much machine vision work was published under the "pattern recognition" rubric, often not considered part of AI).

One reason for this exclusion is that much of AI research was portrayed as an investigation of the intellectual mechanisms used by the human mind, whereas statistical machine learning was (and is) more focused on narrow applications, like image classification. Although of course the connectionist paradigm has always claimed to be emulating the human brain (or trying to), this has always been more about reproducing the structure of the human brain in a different substrate, rather than elucidating how a mind arises from a brain- which is what classical AI is primarily interested in.

So if you're wondering with the most popular textbook on AI doesn't say much about how to build a hot dog classifier, that's because classical AI does not consider that to be a burning question, on which rests our scientific understanding of the human mind.