Signal Processing in the World of AI Inbar Levy – Application Engineer, Systematics

Machine techniques and deep learning are driving the current mega-trend of artificial intelligence in the medical device and pharma industries.

In this workshop, you’ll learn how to develop AI applications using MATLAB even if you don’t have a formal background in Machine and Deep Learning.
Our goal is to introduce you to several different tools and approaches for developing predictive models of biomedical signals. We’ll focus on covering all stages of the AI process from end-to-end – i.e. signal research to deployment.
In this practical session, we will deal with essential topics including:

Automatic labeling for biomedical signals of time series
Building Deep Learning Models Using CNNs and LSTMs for Biomedical Signal Data
Importing external models for deep learning from Python into MATLAB – using TensorFlow/PyTorch
Apply advanced pre-processing signal techniques to extract automated features
Using Spectrogram and Continuous Wavelet Transform Techniques (Converting Signals into Images, Inserting Them into a Network, etc.)
Optimizing and running multiple experiments for advanced training