ADR Awareness Centre

This lecture overviews Kernel Methods that have many applications in classification and clustering. It covers the following topics in detail: Kernel Trick. Kernel Matrix. Kernel PCA. Kernel correlation and its use in object tracking. Kernel k-means.

This lecture overviews Domain Adaptation that has many applications in DNN training and adaptation.

This lecture overviews Computational Aesthetics  that has many applications in visual arts and computer graphics. It covers the following topics in detail: Neuroaesthetics. Computational Aesthetics. Critical problems in aesthetics.

This lecture overviews Few Shot Object Recognition that has many applications in image classification, when few training data are available.  It covers the following topics in detail: Few-shot Image Learning definitions and methods.

This lecture overviews various concepts related to multiple drone communications: LTE and WiFi communications, LTE communication infrastructure, IP network issues, Security analysis, throughput, latency and quality-of-service issues.

The lecture includes the essential knowledge about how we obtain/get 2D and/or 3D maps that robots/drones need, taking measurements that allow them to perceive their environment with appropriate sensors.

This book provides an introduction and an overview of learning to quantify (a.k.a. “quantification”), the task of training, by means of supervised learning, estimators of class proportions in unlabelled data.

This lecture overviews an important topic of Digital Humanities, namely digital painting analysis that has many applications in painting conservation and in the history of arts.

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 Distance-based Classification that has many applications in classification. It covers the following topics in detail: k-Nearest neighbor classification, Nearest neighbor graphs Supervised Learning Vector Quantization, LVQ1/2/3.

This lecture overviews Neural SLAM   that has many applications in robotic and autonomous vehicle localization and mapping.

Motion estimation principals will be analyzed. Initiating form 2D and 3D motion models, displacement estimation as well as quality metrics for motion estimation will subsequently be detailed.