adaptive filters; continuous-time signals and systems; data representation: from multiscale transforms to neural networks; dictionaries in machine learning; digital filter structures and their implementation; discrete-time signals and systems; frames in signal processing; machine learning: review and trends; modern transform design for practical audio/image/video coding applications; multirate signal processing for software radio architectures; nonconvex graph learning: sparsity, heavy tails, and clustering; parametric estimation; random signals and stochastic processes; sampling and quantization; signal processing over graphs; tensor methods in deep learning; Engineering (all); Computer Science (all)
Résumé :
[en] Signal processing and machine learning theories are critical enablers for implementing many amazingly sophisticated technological advances. In this chapter we attempt to provide a brief introduction to some key topics in these dynamic areas, which are further discussed in this book. Together, they create a rich toolbox to pave the way for future societal developments.
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