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.

Signals, Systems, and Learning
Estimated 8 weeks
6–8 hours per week
Progress at your own speed

About this course

Skip About this course

Coming Soon March 2021. 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.

At a glance

  • Institution: RICEx
  • Subject: Computer Science
  • Level: Intermediate
  • Prerequisites:

    Linear algebra (matrix manipulation, eigendecomposition, vector spaces, inner products)

    Calculus (integrals, sequences, and series)

What you'll learn

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  • 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

About the instructors

Who can take this course?

Unfortunately, learners from one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.