Displaying 266 resources

Introduction to Machine Learning

This lecture will cover the basic concepts of Machine Learning to alleviate inconsistencies towards concept and notation accuracy. Supervised, self-supervised, unsupervised, semi-supervised learning. Multi-task Machine Learning.
Category
Systems, methodologies, hardware, and tools
Source
AI-OnDemand

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.
Category
Systems, methodologies, hardware, and tools
Source
AI-OnDemand

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.
Category
Systems, methodologies, hardware, and tools
Source
AI-OnDemand

Kernel methods

This lecture overviews Kernel Methods that have many applications in classification and clustering. It covers the following topics in detail: Kernel Trick. Kernel Matrix. Kernel PCA. Kernel correlation and its use in object tracking. Kernel k-means.
Category
Systems, methodologies, hardware, and tools
Source
AI-OnDemand

Data Clustering

This lecture overviews Data Clustering  that has many applications in e.g., facial image clustering, signal/image clustering, concept creation.  It covers the following topics in detail: Clustering Definitions.
Category
Systems, methodologies, hardware, and tools
Source
AI-OnDemand

Position paper on AI for the operation of critical energy and mobility network infrastructures

Category
Action and Interaction Technologies, Systems, methodologies, hardware, and tools, Reasoning and decision-making Technologies, Public reports
Target audience
professionals, Researchers and Academic
Source
Adra-e