Object Shape Classification Utilizing Magnetic Field Disturbance and Supervised Machine Learning

Various narrow artificial intelligence architectures are on the rise due to the development of Graphics Processing Units and, thus, computational capabilities. Massive number multiplication capabilities of GPUs enabled researches to create more complicated and advanced algorithms. Initially, a gaming hardware became a base for modern time Industrial Revolution. Machine learning, once a forgotten branch of computer science, attracts huge investments and interest. In 2014, Google acquired an UK-based start-up Deep Mind for over £400M. In 2016 Volkswagen invested $680M in autonomous vehicle and cyber security start-ups (1). Same year Microsoft announced a newly created AI fund (2) and in May this year it resulted in investment of $7.6M in Bonsai, an AI start-ups that hopes to help companies to integrate machine learning in the infrastructure (3). It seems that almost never-ending pockets of investors are motivated by a promise of automation of difficult tasks, which, until now, have never been performed by humans. This thesis explores various supervised machine learning algorithms, beginning with the simplest k-Nearest Neighbours and Multi-layer Perceptron, to the state of the art architecture created by the industry experts (Deep Residual Network from Microsoft Research), and prominent academic figures (i.e. GG from Oxford). Furthermore, the author of the thesis proposes two additional network structures, named Deep Inception and Stacked Artificial Residual Architecture, inspired by previously mentioned research