Machine Learning for Signal Processing: Data Compression and Denoising

Machine Learning for Signal Processing: Data Compression and Denoising

In this meetup, we will understand how to use machine learning tools for signal processing. In particular: data compression and noise removal. To do so, we will discuss Principal Component Analysis (PCA) and explore how linear algebra can be used for these and other applications.

In this meetup, we will understand how to use machine learning tools for signal processing. In particular: data compression and noise removal. To do so, we will discuss Principal Component Analysis (PCA) and explore how linear algebra can be used for these and other applications.

Presenter Bio: Sara holds a bachelor’s degree in electrical engineer and a Ph.D. in engineering from the University of São Paulo (Brazil). She worked at IBM and is currently working with Deep Learning solutions for Shell and IBM in the Research Centre for Gas Innovation and the Center for Artificial Intelligence. She has been working as a mentor and instructor of Data Science and Machine Learning courses for over 4 years.

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