Signals, Systems, and Learning
Learn the mathematical backbone of data science. Signals, systems, and transforms: from their theoretical mathematical foundations, to practical implementation in circuits and computer algorithms, to machine learning algorithms that convert signals into inferences.
Data science is of growing importance in every STEM field. While data science tools are more readily available now than ever before, properly using these tools requires a mathematical understanding of the algorithms within. This class develops a principled approach to using the terminology, models, and algorithms found in signal processing and machine learning, the mathematical backbone of data science.
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