Standard
Track updates
AS ISO/IEC 23053:2023
[Current]Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML)
AS ISO/IEC 23053:2022 identically adopts ISO/IEC 23053:2022, which establishes an Artificial Intelligence (AI) and Machine Learning (ML) framework for describing a generic AI system using ML technology. The framework describes the system components and their functions in the AI ecosystem. This document is applicable to all types and sizes of organizations, including public and private companies, government entities, and not-for-profit organizations, that are implementing or using AI systems
Published: 20/01/2023
Pages: 34
Table of contents
Cited references
Content history
Table of contents
Header
About this publication
Preface
Foreword
Introduction
1 Scope
2 Normative references
3 Terms and definitions
3.1 Model development and use
3.2 Tools
3.3 Data
4 Abbreviated terms
5 Overview
6 Machine learning system
6.1 Overview
6.2 Task
6.2.1 General
6.2.2 Regression
6.2.3 Classification
6.2.4 Clustering
6.2.5 Anomaly detection
6.2.6 Dimensionality reduction
6.2.7 Other tasks
6.3 Model
6.4 Data
6.5 Tools
6.5.1 General
6.5.2 Data preparation
6.5.3 Categories of ML algorithms
6.5.3.1 General
6.5.3.2 Neural network
6.5.3.2.1 General
6.5.3.2.2 Feed forward neural networks
6.5.3.2.3 Recurrent neural network
6.5.3.2.3.1 General
6.5.3.2.3.2 Long short-term memory networks
6.5.3.2.4 Convolutional neural network
6.5.3.2.5 Structured perceptron
6.5.3.2.6 Deep Boltzmann machine
6.5.3.2.7 Capsule network
6.5.3.2.8 Generative adversarial network
6.5.3.3 Bayesian network
6.5.3.4 Naïve Bayesian algorithm
6.5.3.5 Support vector machine
6.5.3.6 Decision trees
6.5.4 ML optimisation methods
6.5.4.1 General
6.5.4.2 Gradient descent methods
6.5.4.3 Newton’s method
6.5.4.4 Conjugate gradient
6.5.4.5 Gaussian processes
6.5.4.6 Least square curve fitting
6.5.4.7 Maximum likelihood estimation
6.5.4.8 Expectation-maximisation
6.5.5 ML evaluation metrics
6.5.5.1 General
6.5.5.2 Precision, recall, sensitivity and specificity
6.5.5.3 F1 score
6.5.5.4 Accuracy
6.5.5.5 Receiver operating characteristics and area under the curve
6.5.5.6 Confusion matrix
6.5.5.7 Kappa coefficient
6.5.5.8 Matthew’s correlation coefficient
7 Machine learning approaches
7.1 General
7.2 Supervised machine learning
7.3 Unsupervised machine learning
7.4 Semi-supervised machine learning
7.5 Self-supervised machine learning
7.6 Reinforcement machine learning
7.7 Transfer learning
8 Machine learning pipeline
8.1 General
8.2 Data acquisition
8.3 Data preparation
8.4 Modelling
8.5 Verification and validation
8.6 Model deployment
8.7 Operation
8.8 Example machine learning process based on ML pipeline
Annex A
A.1 General
A.2 Data flows in the supervised machine learning process
A.2.1 General
A.2.2 Data flow descriptions
A.2.2.1 Data flow 1
A.2.2.2 Data flow 2
A.2.2.3 Data flow 3
A.3 Data use in machine learning
A.3.1 General
A.3.2 Example data use statement A
A.3.3 Example data use statement B
A.3.4 Example data use statement C
Bibliography
Cited references in this standard
[Current]
Information technology - Artificial intelligence - Artificial intelligence concepts and terminology
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