We are excited to announce the culmination of a series of talks by UMass Applied Math MS students Connor Amorin, Gabriel P. Andrade, Chris Brissette, Matthew Gagnon, Brandon Iles, Jimmy Smith, and Lance Wrobel about their research in biological signal processing and novel classification methods using machine learning. The talk will be held April 22nd, 2018 at our usual room 1667 of the W.E.B. DuBois library.
Abstract: During two talks given over the last few months we introduced the problem of classifying EMG signals and built some intuition about how certain recurrent networks perform the tasks that they do. This time we will wrap everything up by demonstrating how these recurrent systems can be used to classify time series data (like EMG), how well they do, why the results might be what they are, and how these results can be improved. Furthermore, we will discuss considerations that should be taken into account when deciding between one model vs. another. As has been the case during the previous talks, emphasis will be put on recurrent neural networks (RNNs) and reservoir computing but no prior knowledge is assumed.
This represents an interesting new approach at the intersection of biology, math, computer science, and applied machine learning, and we can’t wait to hear about how their methods approached and discuss the merits of using neural networks for time series classification tasks, how the results could be approved, and additionally useful considerations to take into account when choosing between multiple models.
Check out the event and RSVP on the Western Mass Data Science and Statistics Meetup page. See you there!