Adaptive Machine Learning (AML) is a hands-on tutorial that introduces real-time, incremental learning techniques for streaming and continually evolving data. Using CapyMOA, an open-source Python library, participants will explore practical tools and algorithms that adapt to changing data distributions, enabling robust, low-latency learning in dynamic environments. Ideal for researchers and practitioners aiming to build scalable, adaptive solutions.
Presenters: Heitor Murilo Gomes (Victoria University of Wellington), Anton Lee (Victoria University of Wellington), Yibin Sun (University of Waikato)
This tutorial introduces core principles and practical tools for learning in dynamic, real-time environments.
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Machine Learning for Data Streams: Process data arriving continuously and rapidly. Stream learning algorithms update incrementally, operate under memory constraints, and adapt to concept drift.
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Online Continual Learning: enables machine learning models to learn sequentially from non-stationary data while maintaining past knowledge. This tutorial presents a practical guide to OCL, covering key concepts, challenges such as catastrophic forgetting and stability-plasticity trade-off, and recent advances including prototype-based methods and prompt-based approaches. The session includes hands-on demonstrations using CapyMOA, an open-source platform for online/stream/continual learning. By bridging research insights with practical implementation, this tutorial aims to equip attendees with the tools to develop robust OCL solutions.
