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Fundamentals of AI

  1. Chapter 1 : Introduction
    • The Foundations of Artificial Intelligence
    • The History and Evolution of Artificial Intelligence
    • The State-of-the-Art: Models
    • Agents of AI
    • Hype vs Reality
    • Day-to-day interactions with AI
    • Types of AI : Rule-based & Learning-based systems
    • Neural Networks – The Brain Behind Artificial Intelligence
    • Reinforcement Learning
    • Generative AI
    • Agentic AI
  2. Chapter 2 : Solving Problem by Searching
    • Problem Solving Agents
    • Informed and Uninformed (Blind) Search
    • Basics to understand searches
    • Breadth-First Search (BFS)
    • Depth-First Search (DFS)
    • Uniform-Cost Search (UCS) – Romania Map Problem
    • 8-Puzzle Problem
    • Best-First Search (BFS)
    • Understanding Heuristic Values in Best-First Search
    • A* Algorithm
    • Overestimation and Underestimation in Heuristic in A*
  3. Chapter 3 : Machine Learning
    • Linear Regression
    • Regression Performance Metrics
    • Classification using Decision Trees
    • K-Means Clustering.
    • Classification metrics
  4. Chapter 4 : AI Ethics & Responsible AI
    • Introduction to AI Ethics
    • Bias in AI
    • What Is Fairness in AI?
    • What Is Data Privacy?
    • Explainability in AI
    • Sustainability in AI
    • Responsible AI
    • GenAI Usage

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