Displaying 190 resources
Probabilistic Logics to Neuro-Symbolic Artificial Intelligence
A central challenge to contemporary AI is to integrate learning and reasoning.
Federated Learning
This lecture overviews that has many applications in distributed Machine Learning and privacy protection.
Deep Reinforcement Learning
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.
Recurrent Neural Networks. LSTMs
This lecture overviews Recurrent Neural Networks and Long Short-Term Memory (LSTM) networks that have many applications in signal and video analysis. It covers the following topics in detail: Neural Networks for Sequence Analysis.
Multilayer perceptron. Backpropagation
This lecture covers the basic concepts and architectures of Multi-Layer Perceptron (MLP), Activation functions, and Universal Approximation Theorem.
Digital Pathology: On the intersection of Computer Vision and Data Science
Due to the proliferation of whole-slide-imaging (WSI) digital scanners it is now possible to leverage computer vision, image analysis, and machine learning techniques, such as deep learning to process the digital pathology images in hopes to derive