This thesis reviewed fundamental principles of Machine learning, including the Inexact Restoration method and Hinge loss Binary Classification method. The IRNS algorithm was implemented and tested on real data problems, showing advantages in terms of computational cost. The method was also tested on an IoT dataset, showing benefits of adaptive sample size and second order information.