Data assimilation refers to a particular class of inverse problems in which the unknown parameter is the initial condition, or entire solution trajectory, of a (possibly stochastic) dynamical system. The data comprises partial and noisy observations of the trajectory. Often, but not always, the goal of data assimilation is to perform the parameter inference sequentially as data is acquired.  A primary use of data assimilation is in forecasting, where the purpose is to provide better future estimates than can be obtained using either  the data or the dynamical systems model alone. This lecture will overview 
recent advances in the use of machine learning to enhance, or develop new, data assimilation algorithms.