Course Outline
1. Introduction to Artificial Intelligence
-
Defining Artificial Intelligence
-
Examples in daily operations
-
Strategic significance of AI
2. Operational Mechanics of AI
-
Core process: data → model → output
-
Learning methodologies:
-
Supervised learning
-
Unsupervised learning
-
Feedback-directed optimization
-
3. Applications in Public and Professional Sectors
-
Text generation and synthesis
-
Visual and document analytics
-
Audio and video identification systems
4. Interaction Protocols with AI Systems
-
Definition and structure of prompts
-
Best practices for prompt engineering
-
Illustrative use cases
5. Ethical Standards and Accountability in AI
-
Risk assessment and mitigation
-
Data security and privacy safeguards
6. Selection and Utilization of AI Tools
-
Accessible platform examples
-
Compliance verification and security standards
7. Future Trajectory and Preparedness
-
Emerging technological trends
-
Critical competencies for future roles
8. AI Impact on Organizational Operations
-
Operational efficiencies and enhancements
-
Workplace disruptions and challenges
-
Cross-industry case studies
This curriculum applies to various AI architectures, including widely adopted platforms such as ChatGPT, Copilot, and comparable systems.
Requirements
Basic computer proficiency
Testimonials (2)
the trainer, the amount and the quality of information
ALINA UNGURU
Course - Introduction to Artificial Intelligence for Non-technical users
Hands on examples