The document provides a comprehensive overview of artificial intelligence, covering its definitions, areas including machine learning and natural language processing, and methodologies like search problems and constraint satisfaction. It discusses various algorithms such as minimax and reinforcement learning, highlights the importance of Bayesian networks for uncertainty handling, and introduces concepts in deep learning. Key applications and frameworks like Markov decision processes and genetic algorithms are also presented.

1. An Introduction to Artificial
Intelligence
A Comprehensive Overview

2. Introduction: Philosophy of AI,
Definitions
• Philosophy of AI:
- Examines the nature and function of AI
- Key questions: Can machines think? What is
intelligence?
Definitions:
- AI is the simulation of human intelligence by
machines
- Key areas: Machine Learning, Natural Language
Processing, Robotics, etc.

3. Modeling a Problem as Search
Problem, Uninformed Search
• Modeling a Problem as a Search Problem:
- Define states, actions, goal, and cost
- Example: Pathfinding in a maze
Uninformed Search:
- Algorithms: Breadth-First Search, Depth-First
Search
- Characteristics: Do not use problem-specific
knowledge

4. Heuristic Search, Domain
Relaxations
• Heuristic Search:
- Use heuristics to improve search efficiency
- Algorithms: A*, Greedy Best-First Search
Domain Relaxations:
- Simplifying the problem to make it more tractable
- Example: Ignoring obstacles in pathfinding

5. Local Search, Genetic Algorithms
• Local Search:
- Search for solutions in the local neighborhood
- Algorithms: Hill Climbing, Simulated Annealing
Genetic Algorithms:
- Inspired by natural evolution
- Use crossover, mutation, and selection operators

6. Adversarial Search
• Adversarial Search:
- Used in games and competitive environments
- Algorithms: Minimax, Alpha-Beta Pruning
- Example: Chess, Go

7. Constraint Satisfaction
• Constraint Satisfaction Problems (CSP):
- Define variables, domains, and constraints
- Algorithms: Backtracking, Constraint Propagation
- Applications: Scheduling, Sudoku

8. Propositional Logic & Satisfiability
• Propositional Logic:
- Formal system for reasoning about propositions
- Connectives: AND, OR, NOT, IMPLIES
Satisfiability (SAT):
- Determine if there exists an assignment that
satisfies a given formula
- Algorithms: DPLL, SAT Solvers

9. Uncertainty in AI, Bayesian
Networks
• Uncertainty in AI:
- Handling incomplete or uncertain information
- Techniques: Probability Theory, Fuzzy Logic
Bayesian Networks:
- Graphical models representing probabilistic
relationships
- Nodes represent variables, edges represent
dependencies

10. Bayesian Networks Learning &
Inference, Decision Theory
• Bayesian Networks Learning:
- Methods: Maximum Likelihood, Bayesian
Estimation
Inference in Bayesian Networks:
- Techniques: Variable Elimination, Belief
Propagation
Decision Theory:
- Framework for making rational decisions under
uncertainty
- Concepts: Utility, Expected Value

11. Markov Decision Processes
• Markov Decision Processes (MDP):
- Framework for modeling decision making in
stochastic environments
- Components: States, Actions, Transition Model,
Reward Function
- Solution: Policy, Value Function

12. Reinforcement Learning
• Reinforcement Learning (RL):
- Learning by interacting with an environment
- Key concepts: Reward, Policy, Value Function
- Algorithms: Q-Learning, SARSA, Deep Q-Networks
(DQN)

13. Introduction to Deep Learning &
Deep RL
• Deep Learning:
- Subfield of machine learning using neural
networks with many layers
- Applications: Image Recognition, Natural
Language Processing
Deep Reinforcement Learning (Deep RL):
- Combining deep learning and reinforcement
learning
- Applications: Game Playing, Robotics