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Course Outline

The History of Artificial Intelligence

  • Intelligent Agents

Problem Solving Techniques

  • Search-Based Problem Resolution
  • Advanced Search Strategies
  • Competitive Search Algorithms
  • Constraint Satisfaction Challenges

Knowledge Management and Reasoning

  • Logic-Based Agents
  • First-Order Logic Systems
  • Logical Inference Processes
  • Traditional Planning Methods
  • Real-World Planning and Execution
  • Structuring and Representing Knowledge

Reasoning with Uncertain Information

  • Measuring and Modeling Uncertainty
  • Statistical Reasoning Methods
  • Temporal Probability Models
  • Simplified Decision-Making Processes
  • Complex Decision Scenarios

Machine Learning

  • Learning Through Examples
  • Integrating Prior Knowledge
  • Building Probabilistic Models
  • Reinforcement-Based Learning

Interaction, Sensing, and Execution;

  • Processing Natural Language
  • Language for Human-Machine Interaction
  • Sensory Perception Systems
  • Robotic Applications

Summary and Reflections

  • Philosophical Underpinnings of AI
  • AI: Current State and Future Prospects

Requirements

A foundational understanding of computing, biology, mathematics, and physics.

 7 Hours

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