{"id":22941397,"url":"https://github.com/bashmocha/cs50-ai","last_synced_at":"2025-04-01T20:44:05.100Z","repository":{"id":60212802,"uuid":"526616055","full_name":"BashMocha/cs50-ai","owner":"BashMocha","description":"Project submissions for Harvard CS50's Introduction to Artificial Intelligence with Python","archived":false,"fork":false,"pushed_at":"2023-08-10T17:51:24.000Z","size":621,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-25T14:52:43.254Z","etag":null,"topics":["ai","cs50","cs50ai","machine-learning","neural-network","nlp-parsing","nltk-library","optimization","parser","python"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/BashMocha.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-08-19T13:20:45.000Z","updated_at":"2023-08-10T14:51:57.000Z","dependencies_parsed_at":"2024-10-18T03:24:37.961Z","dependency_job_id":null,"html_url":"https://github.com/BashMocha/cs50-ai","commit_stats":null,"previous_names":["bashmocha/cs50-ai","cheesyfrappe/cs50-ai"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BashMocha%2Fcs50-ai","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BashMocha%2Fcs50-ai/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BashMocha%2Fcs50-ai/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BashMocha%2Fcs50-ai/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/BashMocha","download_url":"https://codeload.github.com/BashMocha/cs50-ai/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246709919,"owners_count":20821298,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ai","cs50","cs50ai","machine-learning","neural-network","nlp-parsing","nltk-library","optimization","parser","python"],"created_at":"2024-12-14T13:39:23.812Z","updated_at":"2025-04-01T20:44:05.078Z","avatar_url":"https://github.com/BashMocha.png","language":"Python","readme":"# CS50-AI\n\nProjects for [CS50's Introduction to Artificial Intelligence with Python](http://cs50.harvard.edu/ai/).\n\nSee [CS50's Academic Honesty rules](https://cs50.harvard.edu/college/2021/fall/syllabus/#academic-honesty).\n\n## Projects\n  - Search:\n    - [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)\n    - [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.\n  - Knowledge:\n    - [Knights](https://github.com/CheesyFrappe/cs50-ai/tree/main/1.Knowledge/knights) : Solves three classic Knights and Knave Puzzles using Symbolic Logic.\n    - [Minesweeper](https://github.com/CheesyFrappe/cs50-ai/tree/main/1.Knowledge/minesweeper) : AI to play Minesweeper.\n  - Uncertainty:\n    - [PageRank](https://github.com/CheesyFrappe/cs50-ai/tree/main/2.Uncertainty/pagerank) : Simulates Google's algorithm of ranking different webpages by relevancy.\n    - [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.\n  - Optimization:\n    - [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.\n  - Learning:\n    - [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.\n    - [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.\n  - Neural Networks:\n    - [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.\n  - Language:\n    - [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.\n    - [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.\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbashmocha%2Fcs50-ai","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbashmocha%2Fcs50-ai","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbashmocha%2Fcs50-ai/lists"}