A Theoretical analysis of Recent development in Statistical kind of Machine Learning

The field of Statistical Machine Learning (SML) has seen a significant increase in interest in recent years due to the abundance of data and the growing need for automated decision-making in various management applications. SML techniques, including supervised and unsupervised learning, have been successfully applied to a wide range of management applications such as predictive modeling, customer segmentation, inventory management, fraud detection, marketing optimization and human resource management. This review paper aims to summarize recent advances in SML for management applications, highlighting key trends such as the use of deep learning for predictive modeling, unsupervised learning for customer segmentation, reinforcement learning for inventory management, and the use of Explainable AI (XAI) for transparent and interpretable decision-making. Additionally, this paper also highlights some of the challenges faced in the application of SML in management, including the need for high-quality and labeled data, domain expertise, robust evaluation metrics, and transparency. In conclusion, this review paper provides an overview of the recent advances and challenges in SML for management applications, and it could be useful for researchers and practitioners in the field.