Adaptive Signal Processing: Filtering & Neural Networks

This lecture introduces the foundational concepts of adaptive signal processing, focusing on the derivation of adaptive filters like the LMS filter. The instructor explains the importance of self-similarity in signals, the theory of stochastic processes, and the basics of machine learning. The lecture covers the use of gradient descent to minimize error surfaces, the LMS algorithm for adaptive filtering, and its application in echo cancellation systems. By using noisy gradients, the LMS algorithm can adapt to changing environments efficiently, making it a powerful tool in signal processing and neural network training.