  Machine Learning (ML) techniques are rapidly finding a place among the
methods of High Energy Physics data analysis. Different approaches are explored
concerning how much effort should be put into building high-level variables
based on physics insight into the problem, and when it is enough to rely on
low-level ones, allowing ML methods to find patterns without explicit physics
model.
  In this paper we continue the discussion of previous publications on the CP
state of the Higgs boson measurement of the H to tau tau decay channel with the
consecutive tau^pm to rho^pm nu; rho^pm to pi^pm pi^0 and tau^pm to a_1^pm nu;
a_1^pm to rho^0 pi^pm to 3 pi^pm cascade decays. The discrimination of the
Higgs boson CP state is studied as a binary classification problem between
CP-even (scalar) and CP-odd (pseudoscalar), using Deep Neural Network (DNN).
Improvements on the classification from the constraints on directly
non-measurable outgoing neutrinos are discussed. We find, that once added, they
enhance the sensitivity sizably, even if only imperfect information is
provided. In addition to DNN we also evaluate and compare other ML methods:
Boosted Trees (BT), Random Forest (RF) and Support Vector Machine (SVN).
