Displaying 296 resources
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.
Transform Video Compression
This lecture overviews Transform Video Compression that has many applications in digital TV broadcasting, videoconferencing, video streaming and social media.
Multilayer perceptron. Backpropagation
This lecture covers the basic concepts and architectures of Multi-Layer Perceptron (MLP), Activation functions, and Universal Approximation Theorem.
Soccer Video Analysis
This lecture overviews Soccer Video Analysis that has many applications in Human-centered Computing, Image and Video Analysis and Sports Analytics/coaching.
Distance-based Classification
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.
Digital Image Formation
This lecture overviews Image Formation, which is of primary importance in ensuring image quality and enabling image processing. It covers the following topics in detail: Light reflection models. Camera structure and models, e.g., Pinhole Camera.
Multiple Drone Media Production
This lecture overviews Multiple Drone Media Production that has many applications in drone cinematography.
Autonomous Car Sensors
This lecture overviews Autonomous Car Sensors that has many applications in autonomous car perception.
Dimensionality Reduction
This lecture overviews Dimensionality Reduction that has many applications in object clusring and object recognition. It covers the following topics in detail: Feature selection. Principal Component Analysis. Linear Discriminant Analysis.
Motion Estimation
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.