Correlation matrices are powerful tools for understanding the linear relationships between multiple variables in a dataset. They provide a compact way of representing how each variable is associated with every other variable. The primary use of a correlation matrix is to evaluate which pairs of variables have a high or

The Kalman filter, named after its creator Rudolf E. Kalman, is a mathematical algorithm that provides recursive means to estimate the state of a system in a way that minimizes the mean squared error. It's widely used in robotics, economics, control systems, and computer vision. In the realm of computer

Principal Component Analysis (PCA) is a dimensionality reduction technique that's widely used in machine learning and statistics. It works by finding a new set of dimensions (or "principal components") that capture the most variance in the data. By reducing the dimensionality of the data, PCA can make data visualization easier,

The world of signal processing is a fascinating blend of mathematics, engineering, and computer science. From audio to images, and even to more abstract concepts like financial time series, the ability to manipulate and analyze signals is crucial. Among the many tools available to the signal processing engineer, the Wavelet

Eigenspaces, a fundamental concept in linear algebra, have a wide range of applications in various domains of science and engineering. This article delves deep into the realm of eigenspaces, from their fundamental understanding to practical applications and code examples in Python. Fundamentals and Intuition An eigenspace of a matrix (or

RANSAC is a robust method for parameter estimation in the presence of outliers. It works by identifying inliers that agree with a model derived from a randomly chosen subset of data. The model with the maximum number of inliers is then chosen as the best model. In this article, we

K-Means Clustering is a highly sought-after machine learning algorithm, celebrated for its simplicity and efficacy, and is primarily used in unsupervised learning tasks. As a partitioning technique, K-Means serves to dissect a dataset into 'K' unique, non-overlapping clusters or subgroups. Unlike supervised learning techniques that rely on pre-known labels, unsupervised

Learn how to do spectrogram in Python using the essential signal processing packages.