A0194
Title: On some mathematical foundations of machine learning algorithm and an application
Authors: Ismail Aydin - Sinop University (Turkey) [presenting]
Olcay Alpay - Sinop University (Turkey)
Abstract: Machine learning is an interdisciplinary research field that includes probability and statistics, linear algebra, calculus (gradient descent), nonlinear partial differential equations (stochastic problems), Fourier transform, signal processing and computer science, among others. The application of machine learning/deep learning algorithms and methods has become widespread in areas such as mental health diagnosis, object recognition, image processing, semantic segmentation, human action recognition, finance, social sciences, operations research, and epidemic management. Machine learning is based on three concepts: data, models, and learning. As in all everyday applications, the amount of data in scientific experiments has increased significantly compared to a decade ago. Although it is expensive and time-consuming to analyze this large amount of data, machine learning algorithms can provide efficient and fast results. We present some mathematical foundations and methods for machine learning algorithms and an application to a suitable dataset.
