Course Outline

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

  • Al in a historical setting and combinatorial technologies
  • Introduction to Al, concepts, narrow and general Al o Different types of Al
  • Al - sense, reason, act
  • The thinking in Al: Machine learning
  • Advanced Analytics vs Artificial Intelligence
  • Looking back, now, forward
  • 4 types of data analytics
  • Analytics value chain
  • Algorithms but without technical jargon
  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
  • Data as fuel for Al
  • Structured and unstructured data o The 5 V's of data
  • Data governance
  • The data engineering platform
  • Just enough to understand the data architecture
  • Big data reference architecture
  • 3 categories of data usage

Al 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 Al projects

  • Al Funnel process
  • Several idea generation approaches
  • Prioritize projects
  • Al project canvas

Running of Al projects

  • Machine learning life cycle
  • Al machine learning canvas
  • When to make and when to buy Al solutions

How to transform to an Al-ready organization

  • Use the Al strategy cycle
  • Dimensions of the Al framework
  • Practical approach to assess the Al maturity of the organization
  • Best organizational structures
  • Benefits of an Al Center of Excellence
  • Skills and competencies

Al and ethics

  • Risks of Al
  • Ethical guidelines
  • Realizing trustworthy AI
  35 Hours


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