🤖 Artificial Intelligence

Welcome to the Artificial Intelligence Lab Repository! 🚀 This repository contains lab exercises, assignments, lecture materials, and reference books for the Artificial Intelligence (AI) course. It is designed to help students and enthusiasts explore fundamental AI concepts, algorithms, and real-world applications.

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📂 Repository Structure

The repository is well-structured into various categories, making it easy to navigate through different topics and materials. Here's an overview:

🔬 AI Lab Exercises

Each lab folder contains practical implementations of AI algorithms and concepts.

• Lab 1: 📌 CityNet Exercise
• Lab 2: 🔍 BFS, DFS, IDDFS, 8-Puzzle
• Lab 3: 🔄 Iterative Deepening Depth-First Search (IDDFS)
• Lab 4: 🧠 Heuristic Search
• Lab 5: 🤖 Greedy Best-First Search & Minimax Algorithm
• Lab 6: 🌳 Alpha-Beta Pruning
• Lab 7: 🎒 Knapsack Problem & Traveling Salesman Problem (TSP)
• Lab 8: 🧬 Genetic Algorithm
• Lab 9: 🌲 Decision Trees (ID3 Algorithm)
• Lab 10: 📊 Linear & Random Forest Regression
• Lab 11: 📈 Weighted Regression
• Lab 12: 🔄 Logistic Regression
• Lab 13: ⚡ Perceptron & XOR Problem
• Lab 14: 🏋️ Backpropagation Algorithm

📚 Course Materials

• Assignments 📄
  • Assignment-1 📑
  • Assignment-2 📑
  • Assignment-3 📑
• Books & References 📖
  • AI Illuminated
  • Machine Learning by Tom Mitchell
  • Neural Networks & Backpropagation Papers
• Quizzes & Exams 📝
  • Quiz Keys 📜
  • Final Exam Topics 🎯
• Lecture Slides 🎓
  • Introduction to AI
  • Problem Solving by Search
  • Adversarial Search
  • Neural Networks & Machine Learning

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🏗️ AI Concepts Covered

Below are the key AI concepts covered in this repository:

🔍 Search Algorithms

• Uninformed Search: BFS, DFS, IDDFS
• Informed Search: A* Algorithm, Greedy Best-First Search
• Adversarial Search: Minimax Algorithm, Alpha-Beta Pruning

📊 Machine Learning Algorithms

• Supervised Learning
  • Linear Regression
  • Logistic Regression
  • Decision Trees (ID3 Algorithm)
  • Random Forest
• Unsupervised Learning
  • Clustering (K-Means, Hierarchical Clustering)
  • Principal Component Analysis (PCA)
• Reinforcement Learning
  • Q-Learning & Policy-Based Learning

🧠 Neural Networks

• Perceptron
• Multi-Layer Perceptron (MLP)
• Backpropagation Algorithm
• XOR Problem Solution

🧬 Evolutionary Algorithms

• Genetic Algorithms (GA)
• Optimization Techniques

🎮 Decision Making & Planning

• Markov Decision Processes (MDP)
• Game Theory in AI

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💡 How to Use This Repository

1. Clone the repository:

   git clone https://github.com/shoaib1522/Artificial-Intelligence-Lab.git

2. Navigate into the directory:

   cd Artificial-Intelligence-Lab

3. Run Python programs from the respective lab folders:

   python Lab_5/Greedy_Best_First_Search.py

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📌 Contribution Guidelines

Feel free to contribute to this repository by submitting pull requests! Contributions in the form of improved code, additional explanations, or new AI-related content are welcome. 😊

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📞 Contact

For any queries, feel free to reach out! 👨‍💻 Author: Shoaib Mughal 📧 Email: [Your Email] 🔗 GitHub: shoaib1522

Happy Learning! 🚀