Lectures related to Wiener Filter: Adaptive Denoising

Covers linear regression topics including confidence intervals, variance, and maximum likelihood approach.

Explores the application of linear algebra in data science, covering variance reduction, model distribution theory, and maximum likelihood estimates.

On Time Series explores Spectral Estimation, Yule Walker method, and ARIMA models.

Introduces the rigorous approach to sensor fusion for modern applications.

Covers the Recursive Least-Squares algorithm with weighted formulation for real-time data updating.

Explains the Jacobi and Gauss-Seidel methods for solving linear systems iteratively.

Covers linear regression, regularization, and probabilistic models in generating labels.

Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.