Displaying 273 resources
Bayesian Learning
This lecture overviews Bayesian Learning that has many applications in pattern recognition and clustering. It covers the following topics in detail: Bayes probability theorem. Bayes decision rule. Bayesian classification.
AI and Computational Politics
The aim of this lecture is to a) define Computational Politics as a discipline lying at the intersection of Political science and Computer science and b) present the use of AI and IT tools in political data analysis.
Computational Politics has vario
Introduction to Multiple Drone Systems
This lecture will provide the general context for this new and emerging topic, presenting the aims of multiple drone systems, focusing on their sensing and perception. Drone mission formalization, planning and control will be overviewed.
Computational Geometry
This lecture overviews Computational Geometry that has many applications in Computer Graphics, Robotics, Geographic Information Systems, CAD/CAM.
Privacy Protection, Ethics and Regulations for Autonomous Cars
This lecture overviews Privacy Protection, Ethics and Regulations that have many applications in Autonomous Cars.
Introduction to 2D Computer Vision
This lecture overviews digital images and 2D Computer Vision (image analysis).
Imitation Learning
This lecture overviews Imitation Learning (IL) that has many applications in Game Development, robotics training, Autonomous Driving and Computational Cinematography.
Hidden Markov Models
This lecture overviews Hidden Markov Models that have many applications in Data Analytics and Signal Analysis. It covers the following topics in detail: Markov Chains. Hidden Markov Chains: Viterbi algorithm, Forward-backward algorithm.
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.
Dynastic Potential Crossover Operator
An optimal recombination operator for two-parent solutions provides the best solution among those that take the value for each variable from one of the parents (gene transmission property).
Robot and Drone Swarms
This lecture overviews Robot and Drone Swarms that has many applications in autonomous systems: cars/drones.
Symbolic, Statistical, and Causal Representations
In machine learning, we use data to automatically find dependencies in the world, with the goal of predicting future observations.