ADR Awareness Centre

This lecture overviews discrete-time Signals and Systems topics. Discrete-time signals are presented: periodic signals, delta signal, unit step signal, exponential signal, trigonometric signals, complex exponential signal.

This lecture overviews Deep Reinforcement Learning that has many applications in, e.g., Game playing agents, Self-driving vehicles, Robotics (Robot cleaners) and Stock exchange agents.

This lecture overviews Probability Theory  that has many applications in a multitude of scientific and engineering disciplines, notably in Pattern Recognition and Machine Learning. It covers the following topics in detail:

This lecture overviews State –Space Equations that has many applications in digital filters, systems theory and deep learning.

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 Explainable AI that has many applications in trustworthy AI systems and autonomous systems.

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

This lecture overviews Signal Sampling that has many applications in signal acquisition, processing and analysis.

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

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

This lecture overviews Orthogonal Signal Transforms. Fourier Series that has many applications in signal processing, analysis and compression.

This lecture overviews Robust Statistics that has many applications in Data Analytics and Digital Signal Processing and Analysis. It covers the following topics in detail: Outliers.