Channel Title: Barry Van Veen Channel Description: My All Signal Processing channel contains short lectures on topics in signal processing. Many of the lectures have also been used with an inverted or "flipped" classroom paradigm at the University of Wisconsin. The playlists provide a systematic progression through the material. Each playlist includes a list of prerequisite playlists for understanding the background material. <video> Singular Value Decomposition and Regularization of Least Squares Problems Description: </video> <video> Foundations of Artificial Intelligence and Machine Learning Course Promo Video Description: I'm very excited about this new short-form course being offered first in April through InterPro at UW-Madison. The past few months I have worked toward developing new content that condenses the key ideas in artificial intelligence and machine learning into digestible, accessible, and actionable insight. I've always enjoyed teaching learners with diverse backgrounds and interests and am looking forward to working with all who enroll. If you want to learn more about this timely and transformative topic, join us! </video> <video> The Singular Value Decomposition and Least Squares Problems Description: </video> <video> Eigendecomposition, Singular Value Decomposition, and Power Iterations Description: </video> <video> The Singular Value Decomposition Description: </video> <video> Signals- The Basics Description: Introductory ideas and notation concerning signals. </video> <video> Convergence, Tracking, and the LMS Algorithm Step Size Description: The convergence and tracking behavior of the LMS algorithm are dependent on the step size parameter applied to the instantaneous gradient. The various performance tradeoffs involved with selecting a step size parameter are discussed. Small step sizes result in small misadjustment, but can have slow convergence and poor tracking performance. Large step sizes can result in unstable iterations. </video> <video> Bias-Variance Tradeoff in Low Rank Approximations Description: </video> <video> Matrix Completion Description: </video> <video> Finding the MMSE Filter Optimum Weights Description: The math of solving the MMSE problem to find the optimal weights. A linear algebra formulation is used to rewrite the mean-squared error as a perfect square, which allows the MMSE weights to be identified by inspection without defining gradients and. This is the matrix equivalent of the "completing the square" method used to find the minimum of a second order polynomial. </video> <video> Introduction to Minimum Mean-Squared-Error Filtering Description: Introduces the basic framework for MMSE filtering and applications to system modeling, equalization, and interference suppression. </video> <video> Principal Component Analysis Description: </video> <video> Properties of the Singular Value Decomposition Description: </video> <video> Solving the Least-Squares Problem with Gradient Descent: the Least-Mean-Square Algorithm. Description: The least-mean-square (LMS) algorithm is an iterative approach to finding the minimum mean-squared error filter weights based on taking steps in the direction of the negative gradient of the instantaneous error. The LMS algorithm is very simple and widely used in adaptive filtering. </video> <video> Network Graphs and Page Rank Algorithm Description: </video>

Description

My All Signal Processing channel contains short lectures on topics in signal processing. Many of the lectures have also been used with an inverted or "flipped" classroom paradigm at the University of Wisconsin.

The playlists provide a systematic progression through the material. Each playlist includes a list of prerequisite playlists for understanding the background material.

Videos from channel