I have enrolled in Machine learning course from coursera. Prof Andrew Ng is really great. I would like to follow book . I am looking for a book which follow the lecture followed by Prof Andrew Ng.
could anyone help me?
Hi,
these are the machine learning books that I used during my Ph.D.
- Neural Networks and Learning Machines (3rd Edition) by Simon O. Haykin. A nice reference book on neural networks, stochastic sampling, support vector machines, etc. I had the second edition and I don't know exactly what is the content of the third.
- Learning from Data: Concepts, Theory, and Methods (2007) by Vladimir Cherkassky and Filip M. Mulier. A very nice book on the statistical learning theory of V. Vapnik, which is explained in a more pedagogical way.
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (2011) by Trevor Hastie and Robert Tibshirani. A more statistical approach to machine learning with good colour illustrations.
- Learning with kernels: Support vector machines, regularization, optimization, and beyond by B Schölkopf, AJ Smola. A book completely devoted to kernel methods and support vector machines, although it is a bit more mathematical than some of the others.
- Pattern Recognition and Machine Learning (Information Science and Statistics) by Christopher M. Bishop.
However, I would simply ask advice to your Professor to know which book is a good reference according to his lectures. He knows better than anyone else which topics he is going to teach.
Best Regards
Hi,
these are the machine learning books that I used during my Ph.D.
- Neural Networks and Learning Machines (3rd Edition) by Simon O. Haykin. A nice reference book on neural networks, stochastic sampling, support vector machines, etc. I had the second edition and I don't know exactly what is the content of the third.
- Learning from Data: Concepts, Theory, and Methods (2007) by Vladimir Cherkassky and Filip M. Mulier. A very nice book on the statistical learning theory of V. Vapnik, which is explained in a more pedagogical way.
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (2011) by Trevor Hastie and Robert Tibshirani. A more statistical approach to machine learning with good colour illustrations.
- Learning with kernels: Support vector machines, regularization, optimization, and beyond by B Schölkopf, AJ Smola. A book completely devoted to kernel methods and support vector machines, although it is a bit more mathematical than some of the others.
- Pattern Recognition and Machine Learning (Information Science and Statistics) by Christopher M. Bishop.
However, I would simply ask advice to your Professor to know which book is a good reference according to his lectures. He knows better than anyone else which topics he is going to teach.
Best Regards
Thank you very much Dr Loris.
I am planning to follow
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (2011) by Trevor Hastie and Robert Tibshirani.
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