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MMSE Filtering and Least-Squares Problems

February 2, 2019 by 3200 Creative

This set of lessons covers a wide ranging set of signal-processing methods for minimum mean-squared error filtering and other applications of least squares problems occurring in estimation and imaging applications. This includes adaptive filtering methods such as the least-mean-square (LMS) algorithm.

Course Content

Lessons Status
1

Introduction to Minimum Mean-Squared Error Filtering

2

Solving for the Minimum Mean-Squared Error Weights

3

Solving Least-Squares Problems with Gradient Descent: the Least Mean-Square Algorithm

4

Convergence, Tracking, and the LMS Algorithm Step Size

Filed Under: Uncategorized

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Course Lessons

  • Introduction to Minimum Mean-Squared Error Filtering

  • Solving for the Minimum Mean-Squared Error Weights

  • Solving Least-Squares Problems with Gradient Descent: the Least Mean-Square Algorithm

  • Convergence, Tracking, and the LMS Algorithm Step Size

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  • Random Signal Characterization

  • Basis Representations of Signals

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  • Introduction to Signal Estimation and Detection Theory

  • MMSE Filtering and Least-Squares Problems

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