AI at the Edge

Index

A

• aborting a project, Calling it quits
• accelerometer, Machine Learning, Motion and Position, Hardware configuration
• accuracy (algorithmic performance metric), Accuracy
• acoustic sensors, Acoustic and Vibration
• active learning, Semi-supervised and active learning algorithms-Semi-supervised and active learning algorithms, Semi-supervised and active learning, Refining an algorithm over time
• active learning loop, Refining an algorithm over time
• adaptive cruise control, Scoping a Solution
• advanced driver-assistance system (ADAS), Scoping a Solution
• AI for Good, Edge AI for Good, AI for Good
• Alexa, Simulated real-world testing
• algorithm bias, Black Boxes and Bias, Accounting for Choices in Design
• algorithmic performance metrics, Algorithmic performance-Mean average precision
  • accuracy, Accuracy
  • confusion matrix, Confusion matrix
  • error metrics, Error metrics-Error metrics
  • F1 score and MCC, F1 score and MCC
  • loss, Loss
  • mean average precision, Mean average precision
  • positive/negative rates, Positive and negative rates
  • precision and recall, Precision and recall-Precision and recall
  • ROC and AUC, ROC and AUC-ROC and AUC
• algorithms, edge AI, Algorithms for Edge AI-Summary
  • anomaly detection, Anomaly detection
  • automated machine learning, Automated machine learning (AutoML)
  • cascades, Combining algorithms
  • classical machine learning, Classical machine learning-Classical machine learning
  • classification, Classification
  • clustering algorithms, Clustering
  • combining algorithms, Combining algorithms
  • conditionals and heuristics, ...