Adaptive Machine Learning

Adaptive machine learning focuses on creating models that can adjust to changing data distributions and environments, addressing limitations of traditional static models. Current research emphasizes techniques like self-labeling, which allows models to automatically adapt to new data without manual intervention, and machine unlearning, which enables the selective removal of data to improve privacy and model robustness. These advancements are crucial for deploying reliable and ethical AI systems in dynamic real-world applications, such as manufacturing, recommendation systems, and network management, improving efficiency and performance while mitigating risks.