I am looking for good introductory and advanced books on unsupervised learning. I have already read books like Probabilistic Graphical Models from D. Kholler and Pattern Recognition and Machine Learning from C. M. Bishop. I am also very familiar with the Ph.D. thesis of K. P. Murphy, on Dynamic Bayesian Networks.

I have read all of the above mostly for the probability aspects, not really the applications to AI and ML. I would like to know what are the good books (or references) for unsupervised learning, that focuses on practical exercises and examples instead of deep and abstract concepts.

  • $\begingroup$ I didn't find any freely available good book that focuses on UL, but this paper/tutorial could be useful. The book Unsupervised Learning: Foundations of Neural Computation (1999, edited by Geoffrey Hinton and Terrence J. Sejnowski) focuses on unsupervised learning algorithms for neural networks. However, I have not read it, so I don't know if it meets your needs. There are other books online, but I don't know whether they are good or not. $\endgroup$
    – nbro
    Jan 17 at 22:12

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