Introduction to Artificial Intelligence

A series of notes in the subject of Artificial Intelligence (AI)

1. Introduction slides
   • Why Artificial Intelligence (AI)
   • The Turning Test
   • Strong AI vs Weak AI
   • History of AI

   

2. Mathematics for AI
   • Linear Algebra slides
   • Probability slides
   • Optimization slides

   

3. Problem Solving,
   • An Example Integration
   \[\]
4. Uninformed Search slides
   • State Space Representation
   • Sparse Representation Graph
   • Depth-First Search
   • Breadth-First Search
   • Combinations

   

5. Informed Search slides
   • Heuristics
   • A*
   • Best First Searches

   

6. Search by Optimization slides
   • Hill Climbing
   • Simulated Annealing
   • Cost Functions and Gradient Descent

   

7. Search Adversarial Games slides
   • Games
   • Minimax
   • Alpha-Beta Pruning

   

8. Constraint Satisfaction Problems (CSP) slides
   • Basic Definitions
   • Constraint Techniques
   • Solving the problems
   • Backtracking slides
   • Branch and Bound
   • Examples of using CSP
     • Domain Reduction
     • Visual Object Recognition

   

9. Bayesian Networks
   • Introduction slides
   • Causality Problem slides
   • Belief Propagation and Junction Trees slides

   

10. Neural Networks
    • Perceptron slides
    • Mulitlayer Perceptron slides
    • Universal Representation Theorem slides
    • Convolutional Networks slides

    

11. Machine Learning
    • Learning slides
    • Linear Models slides
    • Bayesian Models slides
    • Decision Trees slides
    • Clustering slides

    

12. Planning
    • Classic Planning
    • Markov Decision Process
    • Bellman Optimal Equation
    • Algorithms
    • Examples
    \[\]
13. Reinforcement Learning
    • Introduction
    • A K-Armed Bandit Problem
    • Exploration Vs Exploitation
    • Markov Process
    • Dynamic Programming
    • Q-Learning
    • The Deep Learning Approach for Representation
    \[\]

UNDER CONSTRUCTION