This repository contains the exercises and projects I've completed while learning from Harvard's CS50 Introduction to Artificial Intelligence with Python course on YouTube.
Each project explores a key concept in artificial intelligence, ranging from search algorithms to optimization and machine learning.
- Filename:
maze.py - Description: Demonstrates the difference between Depth-First Search (DFS) and Breadth-First Search (BFS) using a maze represented by
#(walls) and spaces (paths). - Extras: The solution path can be visualized using ASCII art or image output for clarity.
- Filename:
hospital.py - Description: Solves an optimization problem where multiple houses are placed on a grid. The task is to find the optimal placement of a limited number of hospitals using Random Restart Hill Climbing to minimize average distance to the nearest hospital.
- Concepts: Search, optimization, heuristics.
- Filename:
banknote.py - Description: Classifies banknotes as real or fake using machine learning algorithms (from
scikit-learn). - Dataset: Obtained from the UCI Machine Learning Repository – Banknote Authentication Dataset.
- Techniques Used: Training/test split, SVM (Support Vector Machines), accuracy analysis.
- Python 3
- scikit-learn
- Matplotlib (for visualizations, optional)
- Random, math libraries for simulation and optimization
- All projects are self-contained and runnable via command line.
- Useful for those learning AI fundamentals through hands-on examples.
All content and project ideas are inspired by Harvard's CS50 AI Course on YouTube.
This project is for educational purposes. Refer to the CS50 license if you plan to reuse their assets.