Abstract

Big data and Machine Learning is a particular topic that focuses on the key optimization challenges that underpin machine learning algorithms. We want to look at how state-of-the-art machine learning and mathematical programming interact, and we're looking for articles that either improve the scalability and efficiency of existing machine learning models or encourage new mathematical programming applications in machine learning. The majority of machine learning issues can be reduced to optimization issues. Consider a machine learning analyst who is attempting to solve an issue using a set of data. The modeler formulates the problem by choosing an acceptable model family and manipulating the data into a modeling-friendly format. The model is then usually trained by solving a core optimization problem that optimizes the model's variables or parameters in relation to the chosen loss function and perhaps a regularization function. The fundamental optimization problem may be solved several times during the model selection and validation process. Through these essential optimization challenges, the research topic of mathematical programming connects with machine learning.