Fondation Intelligence Artificielle (IA) + examen

The following roles could be interested:

• Developers
• Project managers
• Product managers
• Chief information officers
• Chief finance officers
• Change practitioners
• Business consultants
• Leaders of people

 After completing this course you should be able to:

• Grasp the core concepts and terminology of artificial intelligence.
• Recognize ethical, legal, and sustainability issues in AI.
• Identify how AI and machine learning are used in real-world scenarios.
• Understand the basics of data, data quality, and generative AI.
• Discover practical ways AI can be applied in organizations.
• Explore the impact of AI on careers, society, and the future of work.

            1.1 Key AI definitions and terminology

            1.2 Key milestones in AI development

            1.3 Types of AI: narrow vs general

            1.4 AI's impact on society

            1.5 Environmental sustainability in AI

2. Ethical and Legal Considerations   

            2.1 Ethical concerns: bias and privacy in AI

            2.2 Guiding principles for ethical AI development

            2.3 Strategies for addressing ethical challenges

            2.4 The role of regulation in AI

            2.5 Risk management processes in AI

3. Enablers of AI         

            3.1 Common examples of AI in practice

            3.2 The role of robotics in AI

            3.3 Machine learning fundamentals

            3.4 Common machine learning concepts

            3.5 Supervised vs unsupervised learning

4. Finding and Using Data in AI          

            4.1 Key data terminology and concepts

            4.2 Data quality characteristics and importance

            4.3 Data handling risks and mitigation strategies

            4.4 Big data: purpose and applications

            4.5 Data visualization techniques and tools

            4.6 Generative AI terminology

            4.7 Generative AI and large language models

            4.8 Data in the machine learning process

5. Using AI in Your Organization        

            5.1 Identifying AI opportunities in organizations

            5.2 Business case structure and contents

            5.3 Stakeholder identification and categorization

            5.4 Project management approaches for AI

            5.5 Risk, cost, and benefit analysis

            5.6 Ongoing governance activities for AI

6. Future Planning and Impact

            6.1 AI roles and career opportunities

            6.2 Real-world AI applications across industries

            6.3 AI's societal impact and future directions

            6.4 Consciousness and ethical AI considerations

Attendees should meet the following prerequisites:

• Basic Computer knowledge.
• Knowledge of AI terminology is recommended.

Successful completion of the EXIN BCS Artificial Intelligence Foundation exam.

Exam details:

• Exam type: Multiple choice questions
• Number of questions: 40
• Passing score: 65%
• Open book/notes: NoElectronic devices/aids allowed: No
• Exam duration: 60 minutes
• ECTS study points: 2
• Languages: English, Portuguese, Chinese, Dutch, Japanese