I don't know about that. It might appear that way, if you only read news articles on ML since these usually are announcements of the state-of-the-art.
However, compare the topics covered in Duda and Hart's text (1st edition, 1973; 2nd edition 1995) to the more recent text by Hastie et al. (current edition ~2013). There isn't a huge difference in subject matter. The latter is slightly more advanced and has more of a statistics perspective, but the foundations are there: Bayes' decision theory, linear methods for classification / regression, naive bayes, neural networks, decision trees, ensembles, clustering, etc...
There is a range of topics that are foundational to ML, and thus, relatively stable. These topics are built upon the even more solid foundations of probability theory and statistics. The biggest advances in ML in the last decade (I would argue) were not due to advances in theory.
However, compare the topics covered in Duda and Hart's text (1st edition, 1973; 2nd edition 1995) to the more recent text by Hastie et al. (current edition ~2013). There isn't a huge difference in subject matter. The latter is slightly more advanced and has more of a statistics perspective, but the foundations are there: Bayes' decision theory, linear methods for classification / regression, naive bayes, neural networks, decision trees, ensembles, clustering, etc...
There is a range of topics that are foundational to ML, and thus, relatively stable. These topics are built upon the even more solid foundations of probability theory and statistics. The biggest advances in ML in the last decade (I would argue) were not due to advances in theory.