


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



Discrete-time Signals and Systems
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.



Introduction to Signals and Systems
This lecture overviews Signals and Systems. 1D signals, 2D signals (images), 3D signals (videos, medical volumes) are presented. Multichannel signals come next.



Mathematical brain modeling
This lecture overviews Mathematical Brain Modeling that has many applications in Artificial Neural Networks. It covers the following topics in detail: Brain Cells (Sensory and Motor neurons, Interneurons, glia).



Wireless Communication Networks
This lecture overviews Wireless Communication Networks that has many applications in autonomous systems.
Probabilistic Logics to Neuro-Symbolic Artificial Intelligence
A central challenge to contemporary AI is to integrate learning and reasoning.