Proposal Description

In the current era dominated by data-driven decision-making, statisticians are increasingly incorporating Machine Learning (ML) and Artificial Intelligence (AI) techniques to extract meaningful insights from complex datasets. The interpretation of data varies across statistics, machine learning, and artificial intelligence. Statistics delve into past events, while machine learning focuses on predicting future outcomes. Statistics heavily rely on human analysis aided by computers, whereas machine learning leans on computer algorithms with human support. Artificial intelligence, particularly neural networks, seeks to use a single rule to govern all aspects. This session aims to provide fresh insights for machine learning and artificial intelligence by expanding the statistical perspective, creating new dimensions within statistics. It serves as an incubator for findings from diverse angles, fostering mutual benefits among these disciplines. This collaborative initiative seeks more transparent and predictable pathways from data to conclusions, offering a platform for researchers, practitioners, and statisticians to explore the integration of statistical methodologies with ML and AI. Topics of interest include but are not limited to:

1. Innovative Statistical Models in ML/AI: Exploring the development of new statistical models that complement and enhance machine learning and artificial intelligence algorithms.
2. Interdisciplinary Applications: Showcasing successful applications of statistical methods in diverse fields utilizing ML and AI, such as healthcare, finance, environmental sciences, and social sciences.
3. Ethical Considerations: Discussing the ethical implications and challenges in applying statistical techniques within ML and AI frameworks, with a focus on transparency, fairness, and accountability.
4. Data Quality and Preprocessing: Addressing the role of statistics in ensuring data quality, preprocessing, and feature engineering to enhance the performance of ML and AI models.
5. Explainability and Interpretability: Examining statistical approaches for making ML/AI models more interpretable and explainable, fostering trust in their outcomes.
6. Advancements in Statistical Learning: Highlighting recent advancements in statistical learning methods, including ensemble techniques, deep learning, and reinforcement learning.
7. Hybrid Approaches: Exploring methodologies that combine traditional statistical techniques with ML/AI to create hybrid models that capitalize on the strengths of both approaches.

This session invites participants to present their research, case studies, and theoretical developments that contribute to the evolving landscape of statistics in the realm of ML and AI to foster collaboration, share insights, and chart the way forward for an integration of statistical principles with the transformative potential of machine learning and artificial intelligence.