Upcoming MATLAB and Simulink Webinars

Signal Processing for Machine Learning and Deep Learning


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.

In this session we will explore how you can use Signal Processing Toolbox and Wavelet Toolbox for analyzing real world signals. We will also explore how other addon tools like Statistics and Machine Learning Toolbox and Neural Network Toolbox can help for performing machine learning and deep learning.


Using real-data we will explore the following topics:

  • Feature detection and extraction techniques for machine learning workflows
  • Developing predictive models for signals using Deep Learning workflows
  • Leverage high-performance computing resources, such as multicore computers, GPUs, computer to scale up the performance

Please allow approximately 45 minutes to attend the presentation and Q&A session. We will be recording this webinar, so if you can't make it for the live broadcast, register and we will send you a link to watch it on-demand.

About the Presenter

Kirthi K. Devleker is a Product Manager at MathWorks focusing on Signal Processing and Wavelets Toolbox. Kirthi specializes in helping MATLAB users see the value of advanced Signal Processing and Machine Learning techniques applied to sensor data across multiple industry verticals such as medical, aero-defense and other industries. He has been with MathWorks for 8 years; and has a Masters in Electrical Engineering from San Jose State University.

Product Focus

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