Over the past decade, a machine learning technique called deep-learning has gained prominence in

developed an enhanced evolutionary learning method for multi-class object classification to address these

instead. Using a novel approach, we hope to make use of machine learning to help enhance humans cognitive

provides the neurons with the ability to learn. One of the forms of learning that is not well understood is

, showing its utility for automated typology learning and prediction of second language grammatical errors

for visual perception tasks. Key to the success of deep learning is its ability to learn hierarchical

distance metric. In this dissertation, we explore several current problems in distance metric learning and

. Additionally, I show that consensus networks exhibit the ability to hypothesize novel interactions among genes

likelihood of the outputs text sequence. With the recent advancement in a neural network or deep learning

Inspired by human transitive inference and learning ability, whereby two seemingly unrelated