Philipp K. Janert, author of «Data Analysis with Open Source Tools», spends a few pages for explaining how he perceives this «difference».
From his point of view Machine Learning is a fake science. Fragile, secretive and specific techniques for big problems that need secret parameters that have never been published for their application. This parameters will be supplied to you for a price by the inventors-researchers' companies.
In the other hand, statistics is real science, where everything is published and studied by a whole community. A science that has accumulated hundred of years of experience and that offer all its knowledge in any university. The methods offered by statistics are of broad application, robust and open.
And I think he has a point in this reasoning.
PD: Statistics works, ask in any hard sciences. Its contributions has been essential for the science in the last centuries. Machine Learning was bashed (like old AI) because it never offered real solutions or helped us to advance in our understanding of anything. Machine Learning is a tool, not a science, that tries to cope with the limitations of our knowledge, which means that it's a very convenient tool for engineers and problem solvers, as are numerical methods are, but it means too that its results share the problems of numerical methods.
From his point of view Machine Learning is a fake science. Fragile, secretive and specific techniques for big problems that need secret parameters that have never been published for their application. This parameters will be supplied to you for a price by the inventors-researchers' companies.
In the other hand, statistics is real science, where everything is published and studied by a whole community. A science that has accumulated hundred of years of experience and that offer all its knowledge in any university. The methods offered by statistics are of broad application, robust and open.
And I think he has a point in this reasoning.
PD: Statistics works, ask in any hard sciences. Its contributions has been essential for the science in the last centuries. Machine Learning was bashed (like old AI) because it never offered real solutions or helped us to advance in our understanding of anything. Machine Learning is a tool, not a science, that tries to cope with the limitations of our knowledge, which means that it's a very convenient tool for engineers and problem solvers, as are numerical methods are, but it means too that its results share the problems of numerical methods.