Brief Introduction
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.
Description
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.
Knowledge
- 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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