Signals, Systems, and Learning (edX)

Signals, Systems, and Learning (edX)
Course Auditing
Categories
Effort
Certification
Languages
Linear algebra (matrix manipulation, eigendecomposition, vector spaces, inner products) Calculus (integrals, sequences, and series)
Misc

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Signals, Systems, and Learning (edX)
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.

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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.

This course is part of the Elements of Data Science MicroBachelors® Program.


What you'll learn

- Theoretical understanding of data models and systems for processing signals, images, and other data

- Practical implementation of signal processing and machine learning algorithms on data from the real world

- Ability to navigate the data science process as an expert instead of relying on trial and error with black box methods



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MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Course Auditing
214.00 EUR
Linear algebra (matrix manipulation, eigendecomposition, vector spaces, inner products) Calculus (integrals, sequences, and series)

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.