AI-Programming-using-Python

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AI-Programming-using-Python

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This repository contains implementation of different AI algorithms, based on the 4th edition of amazing AI Book, Artificial Intelligence A Modern Approach

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# AI-Programming-using-Python
This repository contains implementation of different AI algorithms, based on the amazing AI book [Artificial Intelligence: A Modern Approach](http://aima.cs.berkeley.edu) (4th edition, 2020).
## Contents

### First part:
- Search problems
    - Uninformed search strategies:
        - BFS, DFS, UCS, DLS, IDS
    - Informed search strategies:
        - Gready, A-star

<img width="85%" src="Search/imgs/sample_solution_path.png">
 Example: solving 8-puzzle using search algorithms.

- Local search:
        - Hill climbing, Simulated Annealing, Genetic algorithms

<img width="50%" src="Search/imgs/50-tsp-sa.gif">
 Example solution found by simulated annealing for TSP problem (for 50 cities).

<img width="100%" src="Search/imgs/10-queens.gif">
 Example: solving 10-Queens using simulated annealing.

- Adversarial search and games
        - MiniMax, alpha-beta pruning

#### Python programming concepts
- Object oriented programming in Python
    - Using and defining special python methods (dunder methods).
    - Immutable objects.
    - Making an object hashable (implementing `__hash__()` and `__eq__()` methods.
    - Implementing decortors in Python.
    - Class methods and static methods.
    - `__repr__()` and `__str__()` methods.

### Second part:
- Machine learning
    - Supervised learning:
        - KNN, Naive Bayes, Desision Trees, SVM, Neural Nets
    - Unsupervised learning:
        - k-Means clustering
    - Reinforcement learning
        - Q-learning

### Example problems
- Search:
    - N-Puzzle, N-Queens, TSP
- Games:
    - Pacman, Chess
- Learning:
    - Pacman, Chess, etc.

### Lesson 1: Uninformed (blind) search strategies
- Implementing data structures like stack, queue and priority queue
- Implementing N-Puzzle (8-Puzzle)
- Implementing uninformed search strategies: BFS, DFS, UCS, DLS, IDS
- Programming assignment

### Lesson 2: Informed search strategies
- Implementing informed search strategies: Greedy, A-star
- Programming assignment

### Lesson 3: Local search
- Implementing N-Queens and TSP(a graphical implementation)
- Implementing local search strategies: hill climbing, simulated annealing and genetic algorithms
- Programming assignment

### Lesson 4: Adversarial search and games
- Implementing Otello
- Implementing adversarial search algoritms: Minimax and alpha-beta prunning
- Programming assignment

## Prerequisite
- Basic knowledge of programming (Python)
- Basic knowledge of data structures and algorithms

## After first part:
- You will have a good understanding of basic AI techniques (solving problems using searching)
- You will become a real python programmer and most importantly a real programmer!
- You will see a lot of programming challenges and you will learn how to solve them

## Reference

AI-Programming-using-Python

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