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Course Outline

Introduction to Artificial Intelligence (AI), Machine Learning (ML), and Data Science

  • Historical evolution of AI and the integration of combinatorial technologies
  • Fundamentals of AI, core concepts, and the distinction between narrow and general AI; classification of AI types
  • Core AI functions: sensing, reasoning, and action
  • Cognitive processes in AI: an overview of machine learning
  • Differentiating advanced analytics from artificial intelligence
  • Historical context, current status, and future projections
  • Classification of the four types of data analytics
  • The analytics value chain framework
  • Explanation of algorithms in plain language, avoiding excessive technical jargon
  • Principles of supervised learning
  • Principles of unsupervised learning
  • Fundamentals of reinforcement learning
  • The role of data as essential input for AI systems
  • Structured versus unstructured data; the 5 V’s of data characteristics
  • Frameworks for data governance
  • Components of a data engineering platform
  • Essential knowledge for understanding data architecture
  • Reference architectures for big data systems
  • Categorization of data usage patterns into three distinct groups

AI Opportunity Matrix

Proven use cases mapped to Porter’s value chain

  • Application within primary activities
  • Application within supporting activities

Proven use cases categorized by technology

  • Natural Language Processing (NLP)
  • Image recognition systems
  • Machine learning applications

Conceptualization and Ideation of AI Projects

  • The AI Funnel methodology
  • Various approaches to generating project ideas
  • Strategies for prioritizing AI initiatives
  • Utilization of the AI project canvas

Execution and Management of AI Projects

  • The machine learning lifecycle
  • Application of the AI machine learning canvas
  • Decision-making frameworks for developing in-house versus procuring external AI solutions

Strategies for Transforming into an AI-Ready Organization

  • Implementation of the AI strategy cycle
  • Key dimensions of the AI governance framework
  • Practical methods for assessing organizational AI maturity
  • Optimization of organizational structures
  • Advantages of establishing an AI Center of Excellence
  • Identification of required skills and competencies

AI and Ethics

  • Identification of risks associated with AI
  • Development of ethical guidelines
  • Strategies for realizing trustworthy AI systems
 35 Hours

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