Tentative Program

This track focuses on innovative statistical methodologies that enhance machine learning models. It aims to explore the integration of classical statistics with modern computational techniques.

This session will delve into the use of predictive analytics across various domains, highlighting case studies and real-world applications. Participants will discuss the statistical foundations that underpin effective predictive modeling.

This track emphasizes the role of statistical inference in data science, particularly in drawing conclusions from data. It will cover both theoretical frameworks and practical implementations.

This session aims to provide insights into the statistical underpinnings of various machine learning algorithms. Discussions will include the evaluation of model performance through statistical metrics.

This track will explore advanced clustering methodologies suitable for large datasets. Participants will examine the statistical challenges and solutions associated with clustering in big data environments.

This session focuses on the application of simulation methods in statistical modeling and analysis. Participants will discuss how simulation can aid in understanding complex statistical phenomena.