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Duration 14 hours
Course Outline
Introduction
General Overview of Artificial Intelligence (AI)
- Machine learning systems
Investigating AI Applications
- Application of AI in corporate environments
Technical Foundations of AI
- Model fitting concepts: underfitting, overfitting, classification, and regularization
- Multi-layer perceptrons (MLP) and deep learning architectures
- Convolutional and recurrent neural networks
Evaluating Strategic Implementation Paths
- Decision-making framework for development versus procurement
- AI maturity models for public sector organizations
Data Management Within Organizational Contexts
- Data readiness assessment
- Word embeddings techniques
- Training methodologies using synthetic data
Criteria for AI Project Selection
- Primary factors for project selection
Governance of AI Projects
- Distinctions between machine learning and deep learning
- Project management frameworks (lifecycle, timelines, methodologies)
- Operational maintenance and risk mitigation strategies
Stakeholder Feedback Mechanisms
- Deployment of feedback collection methods (surveys, interviews)
- Identification of key stakeholders for feedback provision
- Analysis of feedback outcomes
Conclusions and Recommended Actions
Requirements
- No prior prerequisites are required
Target Audience
- Senior business leaders
- Project managers
