Deep Learning for Sensor Data

#2018 #AI #Deep #learning #BYU #Mathworks
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Machine learning and Deep Learning are powerful tools for solving complex modeling problems across a broad range of industries. The benefits of machine learning are being realized in applications everywhere, including predictive maintenance, health monitoring, financial portfolio forecasting, and advanced driver assistance. However, developing predictive models for signals obtained from sensors is not a trivial task. Moreover, there is an increasing need for developing smart sensor signal processing algorithms which can be either deployed on edge nodes / embedded devices or on the cloud depending on the application.


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  • Date: 05 Nov 2018
  • Time: 12:30 PM to 01:30 PM
  • All times are (UTC-07:00) Mountain Time (US & Canada)
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  • Engineering Building
  • Brigham Young University
  • Provo, Utah
  • United States 84604
  • Building: Engineering Building (New Building)
  • Room Number: 402
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  • Co-sponsored by BYU Electrical Engineering Department & Mathworks




Agenda

n this session we will explore how you can use MATLAB for developing predictive models for real world sensor analytics using machine learning and deep learning workflows. Using real data we will explore the following topics:

We will explore the following:

 - How advanced signal processing techniques can be used to obtain deep insights into signal data
 - Signal Pre-processing techniques to increase sensor signal quality.
 - Feature detection and extraction techniques for machine learning workflows
 - Developing predictive models quickly for signals using Deep Learning workflows
 - Leverage high-performance computing resources, such as multicore computers, GPUs, computer clusters to scale up the performance