Usefulness of various logic synthesis methods for Machine Learning is studied. For instance; functional decomposition, and especially Ashenhurst-Curtis decomposition, other decomposition methods, variable ordering and variable partitioning methods, clique covering and graph coloring algorithms, minimum support problem for dimension minimization, acquisition of non-deterministic state machines from examples, deterministic-nondeterministic convertions, encoding methods. BDD algorithms for pattern matching.