ODSC West 2020: Echo State Networks for Time-Series Data
This course is only available to subscribers
Overview
In this session, participants will be introduced to Echo State Networks (a type of recurrent neural network) including theory, key parameters in implementation and practical considerations. Participants will have the opportunity to use a publicly available Echo State Network implementation on open data. Additional results will be shown based on a highly customized implementation.
The tutorial will use python, jupyter notebooks and the EchoTorch python module (https://github.com/nschaetti/EchoTorch).
Participants will come away with a basic understanding of Echo State Networks, how to use the EchoTorch python module, the impact key parameters have on algorithm performance and potential application areas.
Tutorial Overview
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ODSC West 2020: Echo State Networks for Time-Series Data
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Tutorial Overview and Author Bio
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Before you get started: Prerequisites and Resources
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Tutorial Slides
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Echo State Networks for Time-Series Data
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Instructor Bio:
Teal Guidici
Machine Intelligence Scientist | Draper
Teal Guidici, PhD
Teal Guidici is a Senior Machine Intelligence Scientist at Draper where she uses statistical techniques and machine learning algorithms to develop creative solutions for interesting data-driven problems in areas including biomedicine, finance, and remote sensing. Prior to Draper, she did graduate work creating new methods to analyze patterns of co-variation in complex datasets and applied these methods applied to high throughput metabolomics data. She has additional experience in survey design and data analysis in consumer marketing research. Dr. Guidici has a B.S. in Theoretical Mathematics from MIT, a M.S. in Bioinformatics and a Ph.D. in Statistics from the University of Michigan.