簡介／

講題：舞動機器、智慧生命：動態系統學習之美

探索機器學習的新視野！
你聽過動態系統嗎？

從大腦醫學到生物網絡，動態系統的概念被用於促進先進的學習理論和算法。
當機器學習遇到動態系統，又會碰撞出什麼新火花？

逆向工程，打破框架， 啟發未來的學習模式！
千萬不要錯過這精彩的驚奇之旅！

【Abstract】
Machine learning has become a prominent and mainstream area of research in the past decade. More recently, there has been a significant surge of interest in learning dynamical systems from their measurement data. This is attributed to its fundamental importance in various cross-cutting applications from brain medicine for understanding brain dynamics to chronobiology for mapping functional connectivity in large-scale biological networks. On the other side of the spectrum, there is a growing concept of exploiting the notions and tools of dynamical systems to facilitate advanced learning theory and algorithms. These approaches go beyond the limitations of conventional machine learning methods and catalize the emerging idea of inverse engineering. The interconnected concepts of learning for dynamical systems and dynamical systems for learning form the cornerstone for constructing the future building blocks of machine learning. This talk will address theoretical and computational approaches to learning for dynamical systems, and illustrate systems-enabled learning theory and data-driven algorithms. These advancements will be demonstrated through various examples that have substantial practical implications. Some notable examples include geometric embedding of dynamic data, network inference, topological manifold learning, and pattern formation and control in complex ensemble systems enabled by optimal transport.

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