[syn0byte]

I think it's just that calling it "maths" tends to give a false sense of certainty where none is warranted.

Example: All the really clever math you use to make an encryption algorithm is all 100% correct. Then all the really clever math you use to show that it would take the heat death of the universe to crack your clever encryption is 100% correct. The user uses 'password' as the key; How does your crypto stand up to a brute force? Is that your algorithms fault? Did your difficulty proof lie to you?

I know key length is a well understood. In terms of how algorithmically "valid" real world data that can otherwise torpedo entire complex systems, it's as good an example as any.

[Rainymood]

Machine learning is literally mathematics, or more specifically, applied statistics. However, human stupidity can never be ruled out of the equation. Not calling something mathematics while it simply is mathematics is obfuscating the issue.