Artificial Intelligence Fundamentals

This repository contains implementations of various Artificial Intelligence techniques, ranging from classical rule-based systems to modern machine learning algorithms. It serves as a practical guide and codebase for understanding AI logic and decision-making processes.

Key Features

• Search Algorithms: Implementation of basic and heuristic search methods for problem-solving.
• Supervised Learning: Classification models including K-Nearest Neighbors (KNN) and Naive Bayes.
• Neural Networks: Fundamentals of Perceptron learning and multi-layer neural network structures.
• Reinforcement Learning: Basic concepts and agent-based learning environments.

Technical Specifications

• Language: Python (Recommended)
• Algorithms: Rule-Based, Decision Trees, KNN, Naive Bayes, Perceptron
• Concepts: Supervised Learning, Neural Networks, Reinforcement Learning
• Focus: Logic Development and Algorithm Optimization

Installation Guide

1. Clone the Project .. code-block:: bash

   git clone https://github.com/afafirmansyah/artificial-intelligence.git

2. Environment Setup - Ensure you have Python installed on your system. - (Optional) Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install Dependencies - If a requirements file is provided, install the necessary libraries:

   pip install -r requirements.txt

4. Run the Algorithms - Navigate to the specific algorithm folder and run the script:

   python name_of_algorithm.py

License

This project is licensed under the MIT License - see the license.txt file for details.

Contact

Ahmad Fauzi Firmansyah - GitHub: afafirmansyah - LinkedIn: ahmad-fauzi-firmansyah