{"id":415392,"date":"2024-10-20T06:04:55","date_gmt":"2024-10-20T06:04:55","guid":{"rendered":"https:\/\/pdfstandards.shop\/product\/uncategorized\/bs-iso-iec-230532022\/"},"modified":"2024-10-26T11:18:29","modified_gmt":"2024-10-26T11:18:29","slug":"bs-iso-iec-230532022","status":"publish","type":"product","link":"https:\/\/pdfstandards.shop\/product\/publishers\/bsi\/bs-iso-iec-230532022\/","title":{"rendered":"BS ISO\/IEC 23053:2022"},"content":{"rendered":"
PDF Pages<\/th>\n | PDF Title<\/th>\n<\/tr>\n | ||||||
---|---|---|---|---|---|---|---|
2<\/td>\n | National foreword <\/td>\n<\/tr>\n | ||||||
6<\/td>\n | Foreword <\/td>\n<\/tr>\n | ||||||
7<\/td>\n | Introduction <\/td>\n<\/tr>\n | ||||||
9<\/td>\n | 1 Scope 2 Normative references 3 Terms and definitions 3.1 Model development and use <\/td>\n<\/tr>\n | ||||||
10<\/td>\n | 3.2 Tools 3.3 Data <\/td>\n<\/tr>\n | ||||||
11<\/td>\n | 4 Abbreviated terms <\/td>\n<\/tr>\n | ||||||
12<\/td>\n | 5 Overview 6 Machine learning system 6.1 Overview <\/td>\n<\/tr>\n | ||||||
13<\/td>\n | 6.2 Task 6.2.1 General <\/td>\n<\/tr>\n | ||||||
14<\/td>\n | 6.2.2 Regression 6.2.3 Classification 6.2.4 Clustering 6.2.5 Anomaly detection <\/td>\n<\/tr>\n | ||||||
15<\/td>\n | 6.2.6 Dimensionality reduction 6.2.7 Other tasks 6.3 Model <\/td>\n<\/tr>\n | ||||||
16<\/td>\n | 6.4 Data <\/td>\n<\/tr>\n | ||||||
17<\/td>\n | 6.5 Tools 6.5.1 General 6.5.2 Data preparation 6.5.3 Categories of ML algorithms <\/td>\n<\/tr>\n | ||||||
22<\/td>\n | 6.5.4 ML optimisation methods <\/td>\n<\/tr>\n | ||||||
23<\/td>\n | 6.5.5 ML evaluation metrics <\/td>\n<\/tr>\n | ||||||
27<\/td>\n | 7 Machine learning approaches 7.1 General 7.2 Supervised machine learning <\/td>\n<\/tr>\n | ||||||
29<\/td>\n | 7.3 Unsupervised machine learning 7.4 Semi-supervised machine learning <\/td>\n<\/tr>\n | ||||||
30<\/td>\n | 7.5 Self-supervised machine learning 7.6 Reinforcement machine learning <\/td>\n<\/tr>\n | ||||||
31<\/td>\n | 7.7 Transfer learning <\/td>\n<\/tr>\n | ||||||
32<\/td>\n | 8 Machine learning pipeline 8.1 General <\/td>\n<\/tr>\n | ||||||
33<\/td>\n | 8.2 Data acquisition 8.3 Data preparation <\/td>\n<\/tr>\n | ||||||
35<\/td>\n | 8.4 Modelling <\/td>\n<\/tr>\n | ||||||
36<\/td>\n | 8.5 Verification and validation 8.6 Model deployment <\/td>\n<\/tr>\n | ||||||
37<\/td>\n | 8.7 Operation 8.8 Example machine learning process based on ML pipeline <\/td>\n<\/tr>\n | ||||||
40<\/td>\n | Annex A (informative) Example data flow and data use statements for supervised learning process <\/td>\n<\/tr>\n | ||||||
42<\/td>\n | Bibliography <\/td>\n<\/tr>\n<\/table>\n","protected":false},"excerpt":{"rendered":" Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML)<\/b><\/p>\n |