Boots: New Machine Learning Approaches to Modeling Dynamical Systems

Large streams of data, mostly unlabeled.

Machine learning approach to fit models to data. How does it work? Take the raw data, hypothesize a model, use a learning algorithm to get the model parameters to match the data.

What makes a good machine learning algorithm?

  • Performance guarantees: \(\theta \approx \theta^*\) (statistical consistency and finite sample bounds)
  • Real-world sensors, data, resources (high-dimensional, large-scale, ...)

For many types of dynamical systems, learning is provably intractable. You must choose the right class of model, or else all bets are off!

Look into:

  • Spectral Learning approaches to machine learning

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