It seem that the textbook "Machine Learning - A Probabilistic Perspective" uses input and output in a opposite way, is it? Chapter 1 of "Machine Learning - A Probabilistic Perspective" by Kevin Patrick Murphy says


  We now consider unsupervised learning, where we are just given output data, without any inputs. The goal is to discover “interesting structure” in the data; this is sometimes called knowledge discovery. 


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  Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses.


I saw this kind of explanation more times than the one in that book.

It seems that the book uses the terms in a opposite way, where the dataset is viewed as the output of some input, is my understanding right?
No, it is not the case. I’m almost sure that it’s a typo and it should be changed to:

“We now consider unsupervised learning, where we are just given input data, without any outputs.”

It can be deduced by looking at the definition of supervised learning from the book:

“In this section, we discuss classification. Here the goal is to learn a mapping from inputs x to outputs y, where y ∈ {1,...,C}, with C being the number of classes.”