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Course Outline

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

  • AI in a historical context and combinatorial technologies
  • Introduction to AI: concepts, narrow and general AI, and different types of AI
  • AI capabilities: sense, reason, and act
  • The thinking behind AI: Machine learning
  • Advanced Analytics versus Artificial Intelligence
  • Looking back, present, and forward
  • Four types of data analytics
  • The analytics value chain
  • Algorithms explained without technical jargon
  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
  • Data as fuel for AI
  • Structured and unstructured data: the 5 V's of data
  • Data governance
  • The data engineering platform
  • Essential knowledge for understanding data architecture
  • Big data reference architecture
  • Three categories of data usage

AI Opportunity Matrix

Successful Use Cases by Porter's Value Chain

  • Primary activities
  • Supporting activities

Successful Use Cases by Technology

  • NLP
  • Image recognition
  • Machine learning

Ideation of AI Projects

  • AI Funnel process
  • Various idea generation approaches
  • Prioritizing projects
  • AI Project canvas

Running AI Projects

  • Machine learning life cycle
  • AI machine learning canvas
  • Deciding when to build and when to buy AI solutions

Transforming into an AI-Ready Organization

  • Utilizing the AI strategy cycle
  • Dimensions of the AI framework
  • Practical approach to assess organizational AI maturity
  • Optimal organizational structures
  • Benefits of an AI Center of Excellence
  • Required skills and competencies

AI and Ethics

  • Risks associated with AI
  • Ethical guidelines
  • Achieving trustworthy AI
 35 Hours

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