ADR Awareness Centre

This lecture overviews Graph Neural Networks that has many applications in Deep Learning, Signal and Video Analysis, Network Theory, Web Science and Social Media Analytics. It covers the following topics in detail: Introduction to Graphs.

1D convolutions are extensively used in digital signal processing (filtering/denoising) and analysis (also through CNNs).  As their computational complexity is of the order O(N^2), their fast execution is a must.

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

Convolutional Neural Networks form the backbone of current AI revolution and are used in a multitude of classification and regression problems. This lecture overviews the transition from multilayer perceptrons to deep architectures.

This lecture overviews Graph Convolutional Networks (GCN) that have many applications in Deep Learning, Signal and Video Analysis, Network Theory, Web Science and Social Media Analytics.

This lecture overviews Face Detection that has many applications in Human-centered Computing, Image and Video Analysis and Social Media Analytics. It covers the following topics in detail: Face Detection definition: Regression and Classification.

This lecture overviews Adversarial Machine Learning that has many applications in DNN robustness and in privacy protection.

Privacy protection is a very important issue, in the context of social media and GDPR. This lecture overviews the face de-identification problem from an engineering perceptive.

This lecture overviews Fast 3D Convolution algorithms that has many applications in the fast implementation of 3D image and video filtering, 3D CNNs and motion estimation.

This lecture overviews Human Centered Computing that has many applications in Human-Computer Interfaces, Data Analytics and Social Media Analytics. It covers the following topics in detail: Semantic Video Content Analysis.

This lecture covers the basic concepts and architectures of Multi-Layer Perceptron (MLP), Activation functions, and Universal Approximation Theorem.

This lecture overviews MultiDrone Datasets that has many applications in autonomous drone research and development.