Displaying 107 resources



Probability Theory
This lecture overviews Probability Theory that has many applications in a multitude of scientific and engineering disciplines, notably in Pattern Recognition and Machine Learning.
Probabilistic Logics to Neuro-Symbolic Artificial Intelligence
A central challenge to contemporary AI is to integrate learning and reasoning.



Wireless Communication Networks
This lecture overviews Wireless Communication Networks that has many applications in autonomous systems.



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).



Syntactic Pattern Recognition
This lecture overviews that has many applications in data analysis. It covers the following topics in detail: Syntactic Pattern Recognition Systems. Preprocessing Techniques. String-Based Models.



Geometric deep learning
Unlock the world of Geometric Learning and Graph Convolutional Networks (GCNs) in this comprehensive course designed to empower you with cutting-edge knowledge and practical skills.