Iso 42001/22989: Fundamentals Of Artificial Intelligence -Ai

Masterclass on key concepts and terms from ISO 22989 and ISO 42001 for auditing, governing, and explaining AI syste

What you'll learn

Accurately interpret key definitions of ISO/IEC 22989 (AI system, model, dataset, training, inference, explainability, traceability).

Relate these definitions to the requirements of ISO/IEC 42001 (clause 3)

Describe the life cycle of an AI system (conception→retirement) and identify artifacts

Define machine learning algorithm (22989) and identify typical families (SVM, FFNN/CNN/RNN-LSTM neural networks, decision trees, k-NN, basic networks)

Practical exercises in Google Colab and ChatGPT to understand the concepts of the standard

Understanding AI ecosystems, layered architecture, and functions in intelligent systems

Data mining vs. machine learning

Machine learning algorithms and their training

Big data

Learn from the most popular AI tools to understand concepts like ChatGPT, Eleven Labs, Heygen, Sora, Explotion, and more.

Requirements

No prior knowledge or qualifications are required. No programming or advanced mathematics knowledge is required.

Description

In this course, you will master the common AI language according to ISO/IEC 22989 and its practical connection to ISO/IEC 42001 governance. Based on our project sessions (AI ecosystem, lifecycle, symbolic/subsymbolic AI, data quality checking, IoT, and ML algorithms), upon completion, you will be able to:Accurately interpret the official definitions of AI systems, models, data, datasets, training, validation, and deployment.Differentiate between symbolic and subsymbolic AI and recognize hybrid approaches with clear examples.Locate the most common algorithms (SVMs, CNN/RNN/LSTM neural networks, decision trees, Bayesian networks) within the 22989 framework.Explain the lifecycle of an AI system, its artifacts, and the evidence required for auditing (traceability, logging, change control).Apply data quality checking: requirements, typical risks (bias, contamination), and minimum controls.Map terms and artifacts to ISO 42001 Annex A controls (technical documentation, data, monitoring, explainability, human intervention, incidents).Distinguish roles and responsibilities in an FSMS (process owners, data custodians, developers, auditors).Identify common risks (e.g., prompt injection and uncontrolled consumption) and their relationship to FSMS policies and metrics.Build a glossary and "data sheet" templates for models, data, and algorithmic decisions, ready for auditing.Learn from the most popular AI tools to understand concepts such as ChatGPT, Eleven Labs, Heygen, Sora, Explotion, among others.This course is designed to enrich your skills and knowledge, offering you practical and relevant tools for your professional development. Whether you are looking to improve your sustainability skills, or want to delve into advanced water management, this course is your gateway to a field of vital global importance. This course is cataloged within the category of informal education, which includes courses, diplomas, seminars, conferences, etc. We have more than 3000 students from all over the world and an overall rating above 4.4, which makes us proud!

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Definitions – ISO 42001

Lecture 2 Definitions – ISO 42001

Section 3: Definitions – ISO/IEC 22989 Part 1

Lecture 3 Definitions – ISO/IEC 22989 Part 1

Section 4: Practical exercise on AI neural networks - ISO/IEC 22989 Part 2

Lecture 4 Practical exercise on AI neural networks - ISO/IEC 22989 Part 2

Section 5: Definitions - ISO/IEC 22989 Part 3

Lecture 5 Definitions - ISO/IEC 22989 Part 3

Section 6: Main fields of application of AI

Lecture 6 Main fields of application of AI

Auditors in training and compliance officers who need to master terminology before taking audit/management modules.,Quality, risk, and security managers, data governance leaders, and data stewards seeking conceptual precision for internal policies and glossaries.,Product/Project Managers, process owners, and innovation leaders who coordinate AI-powered teams and need to differentiate between systems, models, data, artifacts, and lifecycles.,Legal and digital ethics professionals who require normative AI vocabulary (explainability, traceability, human intervention, biases, drift).,Data engineers/analysts and MLOps who want to align their technical jargon with the regulatory framework of definitions.,Faculty and graduate students in technology, law, management, health, finance, energy, and the public sector who need a solid terminological foundation for research or projects.,Anyone interested in learning technical AI concepts