Machine Learning and Signal Processing

Signal processing has given us a bag of tools that have been refined and put to very good use in the last fifty years. There is autocorrelation, convolution, Fourier and wavelet transforms, adaptive filtering via Least Mean Squares (LMS) or Recursive Least Squares (RLS), linear estimators, compressed sensing and gradient descent, to mention a few. Different tools are used to solve different problems, and sometimes, we use a combination of these tools to build a system to process signals. Machine Learning, or the deep neural networks, is much simpler to get used to because the underlying mathematics is fairly straightforward regardless of what network architecture we use. The complexity and the mystery of neural networks lie in the amount of data they process to get the fascinating results we currently have.