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 Duration 7 hours

Course Outline

Historical Evolution of Artificial Intelligence

  • Intelligent Agent Architectures

Problem Solving Methodologies

  • Search-Based Solution Strategies
  • Advanced Techniques Beyond Classical Search
  • Adversarial Search Algorithms
  • Constraint Satisfaction Problems

Knowledge Representation and Reasoning

  • Logical Agent Implementation
  • First-Order Logic Formulations
  • Inference Mechanisms in First-Order Logic
  • Classical Planning Approaches
  • Planning and Execution in Dynamic Environments
  • Knowledge Representation Structures

Handling Uncertain Knowledge and Reasoning

  • Quantification of Uncertainty
  • Probabilistic Reasoning Frameworks
  • Temporal Probabilistic Reasoning
  • Decision Making in Simple Scenarios
  • Complex Decision Analysis

Machine Learning Paradigms

  • Supervised Learning from Examples
  • Integration of Knowledge in Learning Processes
  • Development of Probabilistic Models
  • Reinforcement Learning Techniques

Communication, Perception, and Autonomous Action

  • Natural Language Processing Systems
  • Natural Language Use in Communication
  • Sensor-Based Perception Capabilities
  • Robotics and Autonomous Systems

Concluding Perspectives

  • Philosophical Foundations of AI
  • Current State and Future Trajectories of AI

Requirements

Foundational proficiency in computer science principles, biological systems, mathematical analysis, and physical sciences is required.

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