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It looks pretty good. The introduction, ANN and Bayesian chapters look just as relevant today as they would have been when this book was published, 25 years ago
by teruakohatu 4y ago
It looks pretty good. The introduction, ANN and Bayesian chapters look just as relevant today as they would have been when this book was published, 25 years ago. I like that hypotheses testing is covered.
Chapter 9, Genetic Algorithms and Programming, was hot stuff back then, and research continues today, I went to an interesting seminar a few months ago given by a genetic programming researcher, but from the perspective of solving a ML problem, less useful to the beginner. If you have a regression of classification problem, few people will be trying genetic algorithms to solve it.
Chapter 10 - 12 is rule based systems, for example Prolog, which again was thought of being the future back then, and as much as I love using Prolog, it is not part of modern ML curriculum.
Chapter 13 is short chapter on RL, which is popular today (DeepMind).
All that said, if a beginner wants to pick one book, I would suggest ISLR2, also free and has a Edx course
https://www.statlearning.com/ https://www.statlearning.com/
https://www.edx.org/course/statistical-learning https://www.edx.org/course/statistical-learning
- harry8 4y agoIs that the same one that was on the stanford edx clone site? edx seems to be putting more behind a paywall and jacking up the price in a way that's pretty disappointing. That one is what $150 for the computer to tell you if the numbers you typed in some boxes is what was expected, for example. I mean, looking up the answer in the back of the book is worth that? The certificate is worth absolutely nothing to anyone save for how it makes them feel.
- 082349872349872 4y agoRL ca. 1961: https://en.wikipedia.org/wiki/Matchbox_Educable_Noughts_and_Crosses_Engine https://en.wikipedia.org/wiki/Matchbox_Educable_Noughts_and_... (which I learned about from a Fred Saberhagen short)