I have this book, and would not recommend it as a stand-alone learning guide. It gives a decent intuitive treatment of some topics but is inconsistent. It will furthermore jump between e.g. an explanation of neural networks without any mathematics to the full derivation of backpropagation. It tries to hit a sweet spot between rigor and intuition but in my opinion it largely fails to bridge the two.
Unfortunately, I'm not aware of any good ML books that are current. Mitchell's was really good but is out of date. Bishop is a megalithic tome of statistical mathematics and is better as a reference than a textbook. I think that a good MOOC course paired with selected readings is the best currently available option.
Just checked out Mitchell's site. Looks like he's been working on a 2nd ed for awhile. he should just open-source the first one since it's so old. But I found he just released all the vids from a ML class he gave in 2011 - wonder if they are as good as Yaser's? http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml
It appears that the deal doesn't apply to eBooks, as there is no place to enter the code. Also, they have gigantic red text informing the potential purchaser that they don't offer Android or "normal" eBooks (PDF, ePub, etc).
This book was recommended to me by a friend (who is a genius and great at ML), and I've just begun reading it.
My problem with MOOC is that I strongly dislike the audio/video format. I love textbooks. I learned a lot of what I know about computer science from books, not lectures. I went through many of Tanenbaum's thoughout high school -- and was more addicted to his textbooks than many of the novels that I read at the time.
I would really like to get some recommendations on some good _textual_ ML material.
Comments
I have this book, and would not recommend it as a stand-alone learning guide. It gives a decent intuitive treatment of some topics but is inconsistent. It will furthermore jump between e.g. an explanation of neural networks without any mathematics to the full derivation of backpropagation. It tries to hit a sweet spot between rigor and intuition but in my opinion it largely fails to bridge the two.
Unfortunately, I'm not aware of any good ML books that are current. Mitchell's was really good but is out of date. Bishop is a megalithic tome of statistical mathematics and is better as a reference than a textbook. I think that a good MOOC course paired with selected readings is the best currently available option.
Just checked out Mitchell's site. Looks like he's been working on a 2nd ed for awhile. he should just open-source the first one since it's so old. But I found he just released all the vids from a ML class he gave in 2011 - wonder if they are as good as Yaser's? http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml
Murphy's is probably most current, and an excellent text (read first review). OTW there's lots of stuff on the web for various levels of rigor
http://www.amazon.com/Machine-Learning-Probabilistic-Perspec...
http://metaoptimize.com/qa/questions/186/good-freely-availab...
http://www.p-value.info/2012/11/free-datascience-books.html
http://www.kaggle.com/wiki/Tutorials
Now is a good time to pick up the Murphy book:
http://mitpress.mit.edu/content/spread-knowledge-sale-detail...
http://mitpress.mit.edu/books/machine-learning-2
It appears that the deal doesn't apply to eBooks, as there is no place to enter the code. Also, they have gigantic red text informing the potential purchaser that they don't offer Android or "normal" eBooks (PDF, ePub, etc).
I appreciate the second perspective.
This book was recommended to me by a friend (who is a genius and great at ML), and I've just begun reading it.
My problem with MOOC is that I strongly dislike the audio/video format. I love textbooks. I learned a lot of what I know about computer science from books, not lectures. I went through many of Tanenbaum's thoughout high school -- and was more addicted to his textbooks than many of the novels that I read at the time.
I would really like to get some recommendations on some good _textual_ ML material.