CS50 AI Course Repository

This repository contains my work for CS50’s Introduction to Artificial Intelligence with Python (CS50 AI). It includes problem sets, notes, experiments, and reflections as I learn core AI concepts such as search, knowledge representation, uncertainty, optimization, machine learning, neural networks, and natural language processing.

Purpose

The purpose of this repository is to:

• Organize all course assignments and supporting materials in one place
• Track progress throughout the course
• Document my learning using a consistent workflow
• Practice software development habits while learning AI

Learning Method: Software Development Learning Cycle

For each problem set and project in this course, I follow a Software Development Learning Cycle to ensure my work is structured, testable, and reflective.

1. Understand the Problem

• Read the full specification carefully
• Identify inputs, outputs, constraints, and evaluation criteria
• Break the task into smaller components
• Identify which AI concept the task focuses on

2. Plan the Solution

• Outline the approach before writing code
• Write pseudocode for core logic
• Choose appropriate algorithms and data structures
• Identify edge cases early

3. Implement

• Write clear, readable Python code
• Follow consistent naming and formatting
• Commit changes frequently with meaningful messages

4. Test and Debug

• Run any provided tests (if available)
• Write additional tests for edge cases
• Debug systematically by isolating variables and verifying assumptions
• Confirm correctness before moving on

5. Reflect and Improve

• Summarize what I learned from the assignment
• Identify mistakes and how they were resolved
• Improve code structure, readability, and efficiency
• Record key takeaway