EXIN BCS Artificial Intelligence Foundation – Including Exam [AIF]
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OVERVIEW
Beyond the technical basics, AI Foundation also dives into real-world applications, ethical challenges, and …
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Vrijwel iedere training die op een onze locaties worden getoond zijn ook te volgen vanaf huis via Virtual Classroom training. Dit kunt u bij uw inschrijving erbij vermelden dat u hiervoor kiest.
OVERVIEW
Beyond the technical basics, AI Foundation also dives into real-world applications, ethical challenges, and the future of AI in the workplace. You’ll gain insights into how organizations are adopting AI, what risks and opportunities exist, and how careers are evolving in this fast-moving field. If you’re interested in understanding AI’s potential and preparing for the BCS Foundation certification, this course will help you build the knowledge and confidence to take your next step.
OBJECTIVES
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.
AUDIENCE
The EXIN BCS Artificial Intelligence Foundation is suitable for individuals with an interest in exploring the functions and abilities of AI, and how these can be used in an organization.The following roles could be interested:
- Developers
- Project managers
- Product managers
- Chief information officers
- Chief finance officers
- Change practitioners
- Business consultants
- Leaders of people
CERTIFICATION
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
CONTENT
1. An introduction to AI and historical development1.1 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
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