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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
 14 Hours

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