Linear Algebra: Basics and Applications

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Related lectures (28)

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Signals & Systems I: Introduction and Signal Processing

Covers introductory lessons on signals and systems, signal processing, and practical applications like image compression and multimedia.

Linear Algebra: Change of Basis Matrices

Explores change of basis matrices in linear algebra, emphasizing the importance of understanding matrix transformations between different bases.

Neural Signals and Signal Processing: Methods and Designs

Explores neural signal processing methods, fMRI designs, GLM analysis, and quality control.

Fourier Transform and Differential Equations

Discusses the Fourier transform and its application to solving differential equations, focusing on the wave equation and its transformations.

Convolutional Neural Networks

Covers convolutional neural networks, filter operations, and their applications in signal processing and image analysis.

Notation and key concepts

Covers the notation and key concepts related to digital images and 2D signal representations.

Properties of Fourier Transform

Explores the properties and applications of the Fourier transform in signal processing and mathematics.

Estimation and Linear Prediction - Part 2

Explores power spectral density, Wiener-Khintchine theorem, ergodicity, and correlation estimation in random signals for signal processing.

Spectral Estimation Methods

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

Signal Processing & Micro-Systems

Covers examples of signal processing, analog signal processing, continuous amplitude modulation, image processing, compression, micro-systems, and medical electronics.