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Friday, December 1, 2017

**Abstract:** Machine learning has become an important technique to understand and predict the trends of large volume of data. While most machine learning models are static, static is hardly the case in real life. We need to create dynamical models by generalizing from past experience and results. In this talk, I will explore the usages of dynamical systems in machine learning. The talk will be divided into two parts. First, I will focus on the Kalman filter, a famous Bayesian model permitting exact inference in a discrete dynamical system, and its extensions. Then, I will discuss how to use continuous dynamical systems as a tool for machine learning, especially for deep neural networks. Some of the results are from my previous internship experience.