Dryads: Next generation tree library using efficient bit abstractions for applications of machine learning

Trees have been known as the most important nonlinear structures that arise in computer science. The
\nDryads project entails building a standard, generic and efficient abstraction of tree algorithms which is
\nstill lacking in most programming languages. Being written in C++ and inline assembly, the project
\nimplements the functionality from efficient bit abstractions at the lowest level to famous machine
\nlearning algorithms like decision trees and KDTrees built on top of this tree library. A separate bit
\nmanipulation library has been written for the project which is scheduled to be standardized in the next
\nversion of C++. This thesis includes implementing algorithms for the C++ STL using the bit manipulation
\nlibrary to demonstrate the speed-up on the current algorithms in the standard as well as an example of
\nhow this new tree library can be used to implement a decision tree, one of the most fundamental
\nmachine learning algorithms. This algorithm was presented at the CppCon 2016 (C++ Conference) in
\nSeattle.