Displaying 302 resources
Convolutional Neural Networks Lecture
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.
Digital Images and Videos
This lecture overviews various digital image types: 2D images, 3D images (videos, medical volumes, hyperspectral images). Multichannel images, e.g., colour and multispectral images and colour theory come next.
Privacy Protection, Ethics and Regulations for Autonomous Cars
This lecture overviews Privacy Protection, Ethics and Regulations that have many applications in Autonomous Cars.
e-Symposium 2023: A methodology for Forecasting Election results from Tweets
Social networks as the virtual equivalent of the ancient agora have become a preeminent space of political discourse. They can nurture new political trends and reveal existing ones.
Few Shot Object Recognition
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.
Active and Passive 3D shape reconstruction methods
This lecture overviews Active and Passive 3D shape reconstruction methods that has many applications in 3D computer vision, autonomous systems perception and medical imaging.
Pedestrian Detection
This lecture overviews Pedestrian Detection that has many applications in autonomous car vision and smart city applications.
An Introduction to PAC-Bayesian Analysis
This resource corresponds to 9th video from the AI Excellence Lecture Series.
PAC-Bayesian Analysis is a framework in machine learning and statistics that combines ideas from the Probably Approximately Correct (PAC) learning framework and Bayesian p
Video Description
This lecture overviews that has many applications in broadcasting archives abd video description, search, retrieval and browsing.
Simultaneous Localization and Mapping
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.
Domain Adaptation
This lecture overviews Domain Adaptation that has many applications in DNN training and adaptation.
Road Traffic Monitoring
This lecture overviews Road Traffic Monitoring that has many applications in in autonomous car perception and smart city management.