ADR Awareness Centre

This lecture will cover the basic concepts of Artificial Neural Networks (ANNs): Biological neural models, Perceptron, Activation functions, Loss types, Steepest Gradient Descent, On-line Perceptron training, Batch Perceptron training.

In this lecture, the limitations of Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) in effectively processing sequences are emphasized.

This lecture overviews  Z Transform that has many applications in signal processing and systems theory.

This lecture presents Laplace Transform (LT) and its region of convergence.  Its relation to Laplace transform is presented. Notable LT properties are reviewed: time shift, convolution, signal differentiation/integration.

This lecture overviews Statistical Detection that has many applications in Machine Learning, Signal Analysis and Statistical Communications.

This lecture overviews the topics of continuous-time periodic signals, signal frequencies and Fourier Transform (FT).  Its relation to Laplace transform is presented.