https://github.com/bashmocha/cs50-ai

Project submissions for Harvard CS50's Introduction to Artificial Intelligence with Python
https://github.com/bashmocha/cs50-ai

ai cs50 cs50ai machine-learning neural-network nlp-parsing nltk-library optimization parser python

Last synced: 19 days ago
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Project submissions for Harvard CS50's Introduction to Artificial Intelligence with Python

• Host: GitHub
• URL: https://github.com/bashmocha/cs50-ai
• Owner: BashMocha
• Created: 2022-08-19T13:20:45.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2023-08-10T17:51:24.000Z (over 1 year ago)
• Last Synced: 2024-12-06T09:38:40.540Z (28 days ago)
• Topics: ai, cs50, cs50ai, machine-learning, neural-network, nlp-parsing, nltk-library, optimization, parser, python
• Language: Python
• Homepage:
• Size: 606 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# CS50-AI

Projects for [CS50's Introduction to Artificial Intelligence with Python](http://cs50.harvard.edu/ai/).

See [CS50's Academic Honesty rules](https://cs50.harvard.edu/college/2021/fall/syllabus/#academic-honesty).

## Projects

  - Search:

    - [Degrees](https://github.com/CheesyFrappe/cs50-ai/tree/main/0.Search/degrees) : Program that determines how many “degrees of separation” apart two actors are, based on [IMBb](https://imdb.com)

    - [Tic-Tac-Toe](https://github.com/CheesyFrappe/cs50-ai/tree/main/0.Search/tictactoe) : Using Minimax game theory, implementation of an AI to play Tic-Tac-Toe optimally.

  - Knowledge:

    - [Knights](https://github.com/CheesyFrappe/cs50-ai/tree/main/1.Knowledge/knights) : Solves three classic Knights and Knave Puzzles using Symbolic Logic.

    - [Minesweeper](https://github.com/CheesyFrappe/cs50-ai/tree/main/1.Knowledge/minesweeper) : AI to play Minesweeper.

  - Uncertainty:

    - [PageRank](https://github.com/CheesyFrappe/cs50-ai/tree/main/2.Uncertainty/pagerank) : Simulates Google's algorithm of ranking different webpages by relevancy.

    - [Heredity](https://github.com/CheesyFrappe/cs50-ai/tree/main/2.Uncertainty/heredity) : Implements a genetic-like algorithm estimating a hidden trait of having a faulty gene based on a visible disability, hearing loss in this case.

  - Optimization:

    - [Crossword](https://github.com/CheesyFrappe/cs50-ai/tree/main/3.Optimization/crossword) : Implements an AI generating crosswords given a template and a dictionary of words.

  - Learning:

    - [Shopping](https://github.com/CheesyFrappe/cs50-ai/tree/main/4.Learning/shopping) : Features an AI to predict whether a customer is likely to complete a purchase with a given csv dataset.

    - [Nim](https://github.com/CheesyFrappe/cs50-ai/tree/main/4.Learning/nim) : Implements an AI agent which learns to play the game of NIM, ie. two players take away rings from several towers, last one to take away a ring loses.

  - Neural Networks:

    - [Traffic](https://github.com/CheesyFrappe/cs50-ai/tree/main/5.Neural%20Networks/traffic) : Loads data from a given dataset; trains and evaluates a simple computer vision neural network that classifies road signs for automated driving.

  - Language:

    - [Parser](https://github.com/CheesyFrappe/cs50-ai/tree/main/6.Language/parser) : Uses the `nltk` library to parse sentences into its basic noun phrase components.

    - [Questions](https://github.com/CheesyFrappe/cs50-ai/tree/main/6.Language/questions) : Parses datasets / corpuses of data by n-grams to understand the word frequencies and meanings. Then, it answers questions with likely answer-sentences from the source dataset.