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Artificial Neural Networks. Perceptron
This lecture will cover the basic concepts of Artificial Neural Networks (ANNs): Biological neural models, Perceptron, Activation functions, Loss types, Steepest Gradient Descent, On-line Perceptron training, Batch Perceptron training.



Real-like MAX-SAT Instances and the Landscape Structure Across the Phase Transition
In contrast with random uniform instances, industrial SAT instances of large size are solvable today by state-of-the-art algorithms.


A comprehensive survey of geometric deep learning
The survey provides a comprehensive overview of deep learning methods for geometric data (point clouds, voxels, network graphs etc.). The relevant knowledge and theoretical background of geometric deep learning is presented first.



Attention and Transformers Networks in Computer Vision
In this lecture focused on Transformers in the field of computer vision, the limitations of Convolutional Neural Networks (CNNs) are emphasized.



Introduction to Autonomous Systems
A fully autonomous system can: a) gain information about the environment, b) work for an extended period without human intervention, c) move either all or part of itself throughout its operating environment without human assistance and d) avoid situa

MOBISERV-AIIA Database
Introduction
Eating and Drinking Activity Recognition Database (MOBISERV-AIIA Database) was created in order to fulfill the need for a good benchmarking database for the evaluation of specialized meal intake (such as eating and drinking) activity rec