Their initial pursuit of a definition of machine learning is a bit scattered, and attempts to weave together the concepts of learning, performance, and knowledge in the contexts of both machine learning and data mining..Tangents are gone off on, but a few select quotes from the ride are shown below..[W]e are interested in improvements in performance, or at least he potential for performance, in new situations.Things learn when they change their behavior in a way that makes them perform better in the future.Learning implies thinking and purpose..Something that learns has to do so intentionally.Experience shows that in many applications of machine learning to data mining, the explicit knowledge structures that are acquired, the structural descriptions, are at least as important as the ability to perform well on new examples..People frequently use data mining to gain knowledge, not just predictions.3 It is not necessarily notable that the term data mining is used as a complementary term to machine learning..The third edition of this text, from which this quote was taken, was published in 2011, when the term data mining had much more traction than it does now; removing references to data mining should still lead to a scenario where what is written above holds true for machine learning itself.Anyhow, while they prefaced their diatribe with their wish to stray from the philosophical, Witten, Frank & Hall actually did a pretty good job of getting somewhat philosophical..Its actually a helpful few excerpts, however, in that it provides a different angle on a machine learning definition: while Mitchell focuses on the specific components of the optimization process, and Goodfellow, Bengio & Courville lean toward a more prescriptive definition noting the relative importance of computing power, this attempt at a definition focuses on what aspects of “learning” are analogous and important in the machine learning process..The selections also offer an important point which is actually just as practical as it is philosophical, in that it is noted, in the final paragraph, that both the acquired knowledge as well as the ability to use this knowledge are important aspects of machine learning (see both training and inference). Christopher BishopLets turn to one final text for its attempt at capturing a definition of machine learning, “Pattern Recognition and Machine Learning,” by researcher Christopher M..Bishop..Of note, Bishop does not explicitly define the term early on, but does a pretty good job of implicitly providing an algorithmic-centric definition of machine learning (note that it is discussed in reference to a digit classification task)..The result of running the machine learning algorithm can be expressed as a function y(x) which takes a new digit image x as input and that generates an output vector y, encoded in the same way as the target vectors..The precise form of the function y(x) is determined during the training phase, also known as the learning phase, on the basis of the training data.. More details