Lectures related to Wiener Filter: Adaptive Denoising

Optimal Least Square Adaptive Filter: FIR Filter

Explores the Optimal Least Square Adaptive Filter, focusing on the Finite Impulse Response (FIR) Filter and strategies to reduce computational burden.

Adaptive Denoising: Echo Cancellation

Explores adaptive denoising techniques, emphasizing echo cancellation and the need for real-time adaptive filters.

Newton's method: Hessian and optimization

Explores Newton's method for optimization, emphasizing the role of the Hessian matrix.

Model Specification in Time Series

Covers the identification and model specification in time series analysis, including AR models and least squares estimation.

Optimization Techniques: Gradient Method Overview

Discusses the gradient method for optimization, focusing on its application in machine learning and the conditions for convergence.

Linear Regression: Basics and Estimation

Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.

Linear Regression

Covers linear regression for estimating train speed using least squares and regularization.

Stochastic Gradient Descent: Optimization and Convergence

Explores stochastic gradient descent, covering convergence rates, acceleration, and practical applications in optimization problems.

Estimation Example: Least Square Estimation

Covers the concept of least square estimation and its real-world applications.

Parametric Estimation in Time Series

Covers parametric estimation in time series analysis, including integrated processes and seasonal modeling.

Linear Equations: Least Squares Solution

Explains how to solve linear equations using the least squares method to minimize errors in the system.

Probabilistic Models for Linear Regression

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Linear Regression: Estimation and Testing

Explores linear regression estimation, hypothesis testing, and practical applications in statistics.

Optimization Methods: Convexity and Gradient Descent

Explores optimization methods, including convexity, gradient descent, and non-convex minimization, with examples like maximum likelihood estimation and ridge regression.

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Spectral Estimation Methods

Explores parametric spectrum estimation methods, including line and smooth spectra, and delves into heart rate variability analysis.

Box-Jenkins Methodology: Building Time Series Models

Covers the Box-Jenkins methodology for building time series models, including model identification, variance calculations, and model diagnostics.

Linear Estimation & Prediction: Models & Methods

Explores linear estimation and prediction in AR parametric models, focusing on Yule Walker equations and Wiener filter.

Numerical Analysis: Introduction to Computational Methods

Covers the basics of numerical analysis and computational methods using Python, focusing on algorithms and practical applications in mathematics.

Estimation and Forecasting in Time Series

Explores estimation, forecasting, and model comparison in time series analysis using real data examples to motivate the study.