After taking the course, diligently attending the classes and doing the assignments, thoroughly reading, and actively participating in discussions, students should be able to:

• Identify key challenging research questions related to advanced machine learning and (AML) general machine intelligence (GMI)
• List methodological difficulties and proposed solutions to building AML/AGI systems
• Explain key components of some AML/GMI architectures, and how these relate to the creation of truly intelligent machines of the future
• Students should have a good idea of:
  • The limitations of current AI methodologies
  • How GMI differs from “narrow AI”
  • Some ongoing AML/GMI projects in industry and academia
  • What the main requirements are for building complete minds
  • What methodologies are currently available and applicable for building complete minds
  • How software architecture plays a central role in AI, robotics, and GMI
  • How to apply presently-known techniques and methodologies for building complex AI systems
  • Emergence, self-organization, and synergism
• Students will have had hands-on experience with:
  • Selected machine learning methods, notably reinforcement learning
  • One programming environment targeting GMI