No and yes, we are both wrong so let's refine our thinking in order to gain accuracy and reach agreement.
Machine learning isn't necessarily probabilistic. This statement definitely is true and I'll prove it.
However my original example
E.g a rule based (causal) parser can learn the structure of a document. alone is unsufficent to qualify it of machine learning! Indeed, an HTML parser alone isn't enough to say it's learning, more accurately it is only memorizing the structure of a page.
ML means that the function that maps inputs to outputs is learned from data.
This definition is overly restrictive (but match e.g the behavior of neural networks).
Wikipedia has a more inclusive and useful definition of what qualifies as ML:
Machine learning (ML) is the study of computer algorithms that improve automatically through experience and by the use of data.[1]
So a ML algorithm allow to automatically improve throughput at a tasks through learning the representation of the data it is fed.
Considering this definition, we can realize that the most used ML algorithm in the world is Pagerank (for improving the ranking of Google search results).
And surprisingly, this algorithm is non-probabilistic.
It attribute higher weight proportionally to URLs that are the most linked to by other sites. I.e it basically learn the structure of the graph that is the web.
And its performance is data driven.
So it's not just about causally memorizing the structure of a graph but also about reusing its past memories for newer queries, then we can effectively talk about machine learning.
Pagerank diagram ->
https://en.m.wikipedia.org/wiki/PageRank#/media/File:PageRan...
I'll give you an example of a non statistical machine learning algorithm that I am currently developing:
Semantic parsing is the task of encoding semantic meaning in a graph from a natural language text input.
This graph can then be used for semantic question answering.
It is rule based and it will learn the semantic structure of the text. The more data you fed it the more knowledge it can encode. Hence it's performance at question answering increase with experience. It is causal and yet it fit the useful definition of machine learning cited above.
ML means that the function that maps inputs to outputs is learned from data. This definition is overly restrictive (but match e.g the behavior of neural networks).
Wikipedia has a more inclusive and useful definition of what qualifies as ML: Machine learning (ML) is the study of computer algorithms that improve automatically through experience and by the use of data.[1] So a ML algorithm allow to automatically improve throughput at a tasks through learning the representation of the data it is fed. Considering this definition, we can realize that the most used ML algorithm in the world is Pagerank (for improving the ranking of Google search results). And surprisingly, this algorithm is non-probabilistic. It attribute higher weight proportionally to URLs that are the most linked to by other sites. I.e it basically learn the structure of the graph that is the web. And its performance is data driven. So it's not just about causally memorizing the structure of a graph but also about reusing its past memories for newer queries, then we can effectively talk about machine learning. Pagerank diagram -> https://en.m.wikipedia.org/wiki/PageRank#/media/File:PageRan...
I'll give you an example of a non statistical machine learning algorithm that I am currently developing: Semantic parsing is the task of encoding semantic meaning in a graph from a natural language text input.
This graph can then be used for semantic question answering.
It is rule based and it will learn the semantic structure of the text. The more data you fed it the more knowledge it can encode. Hence it's performance at question answering increase with experience. It is causal and yet it fit the useful definition of machine learning cited above.