BranchesTags

Repository files navigation

CS50's Introduction to Artificial Intelligence with Python

This repository contains my project solutions for CS50's Introduction to Artificial Intelligence with Python offered by Harvard University.

Course Overview

CS50 AI explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, I gained exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning.

Projects

Week 0: Search

  • Degrees - Determines the shortest path between any two actors by choosing a sequence of movies that connects them
  • Tic-Tac-Toe - Implements an AI to play Tic-Tac-Toe optimally using Minimax

Week 1: Knowledge

  • Knights - Solves logic puzzles using propositional logic
  • Minesweeper - AI to play Minesweeper using knowledge-based agents

Week 2: Uncertainty

  • PageRank - Ranks web pages by importance using the PageRank algorithm
  • Heredity - Assesses the likelihood of a person having a genetic trait using Bayesian networks

Week 3: Optimization

  • Crossword - Generates crossword puzzles using constraint satisfaction

Week 4: Learning

  • Shopping - Predicts whether online shopping customers will complete a purchase using k-nearest neighbors
  • Nim - AI that learns to play Nim through reinforcement learning (Q-learning)

Week 5: Neural Networks

  • Traffic - Neural network to identify traffic signs using TensorFlow/Keras

Week 6: Language

  • Parser - Parses sentences and extracts noun phrase chunks
  • Questions - AI to answer questions given a corpus of text using tf-idf and natural language processing

Technologies Used

  • Python
  • scikit-learn
  • TensorFlow/Keras
  • NLTK (Natural Language Toolkit)
  • Tensorflow

Note

This repository contains only my code implementations. Test data and course materials are not included(I don't want to set github servers on fire)

Acknowledgments

Special thanks to Professor David J. Malan, Brian Yu and the rest of the CS50 team at Harvard University for creating this excellent course.

About

No description or website provided.

Topics

aibayesianneuralnetworkcnncs50ai2024deep-learningharvardxknowledgebase-agentsminimaxnlpnltkpropositional-logicpythonqlearningregressionscikitlearntensorflowtensorflowkeras

Resources

Readme
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
CS50's Introduction to Artificial Intelligence with Python
This repository contains my project solutions for CS50's Introduction to Artificial Intelligence with Python offered by Harvard University.
Course Overview
CS50 AI explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, I gained exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning.
Projects
Week 0: Search
  • Degrees - Determines the shortest path between any two actors by choosing a sequence of movies that connects them
  • Tic-Tac-Toe - Implements an AI to play Tic-Tac-Toe optimally using Minimax
Week 1: Knowledge
  • Knights - Solves logic puzzles using propositional logic
  • Minesweeper - AI to play Minesweeper using knowledge-based agents
Week 2: Uncertainty
  • PageRank - Ranks web pages by importance using the PageRank algorithm
  • Heredity - Assesses the likelihood of a person having a genetic trait using Bayesian networks
Week 3: Optimization
  • Crossword - Generates crossword puzzles using constraint satisfaction
Week 4: Learning
  • Shopping - Predicts whether online shopping customers will complete a purchase using k-nearest neighbors
  • Nim - AI that learns to play Nim through reinforcement learning (Q-learning)
Week 5: Neural Networks
  • Traffic - Neural network to identify traffic signs using TensorFlow/Keras
Week 6: Language
  • Parser - Parses sentences and extracts noun phrase chunks
  • Questions - AI to answer questions given a corpus of text using tf-idf and natural language processing
Technologies Used
  • Python
  • scikit-learn
  • TensorFlow/Keras
  • NLTK (Natural Language Toolkit)
  • Tensorflow
Note
This repository contains only my code implementations. Test data and course materials are not included(I don't want to set github servers on fire)
Acknowledgments
Special thanks to Professor David J. Malan, Brian Yu and the rest of the CS50 team at Harvard University for creating this excellent course.
About
No description or website provided.
Topics
aibayesianneuralnetworkcnncs50ai2024deep-learningharvardxknowledgebase-agentsminimaxnlpnltkpropositional-logicpythonqlearningregressionscikitlearntensorflowtensorflowkeras
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
CS50's Introduction to Artificial Intelligence with Python
This repository contains my project solutions for CS50's Introduction to Artificial Intelligence with Python offered by Harvard University.
Course Overview
CS50 AI explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, I gained exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning.
Projects
Week 0: Search
  • Degrees - Determines the shortest path between any two actors by choosing a sequence of movies that connects them
  • Tic-Tac-Toe - Implements an AI to play Tic-Tac-Toe optimally using Minimax
Week 1: Knowledge
  • Knights - Solves logic puzzles using propositional logic
  • Minesweeper - AI to play Minesweeper using knowledge-based agents
Week 2: Uncertainty
  • PageRank - Ranks web pages by importance using the PageRank algorithm
  • Heredity - Assesses the likelihood of a person having a genetic trait using Bayesian networks
Week 3: Optimization
  • Crossword - Generates crossword puzzles using constraint satisfaction
Week 4: Learning
  • Shopping - Predicts whether online shopping customers will complete a purchase using k-nearest neighbors
  • Nim - AI that learns to play Nim through reinforcement learning (Q-learning)
Week 5: Neural Networks
  • Traffic - Neural network to identify traffic signs using TensorFlow/Keras
Week 6: Language
  • Parser - Parses sentences and extracts noun phrase chunks
  • Questions - AI to answer questions given a corpus of text using tf-idf and natural language processing
Technologies Used
  • Python
  • scikit-learn
  • TensorFlow/Keras
  • NLTK (Natural Language Toolkit)
  • Tensorflow
Note
This repository contains only my code implementations. Test data and course materials are not included(I don't want to set github servers on fire)
Acknowledgments
Special thanks to Professor David J. Malan, Brian Yu and the rest of the CS50 team at Harvard University for creating this excellent course.
About
No description or website provided.
Topics
aibayesianneuralnetworkcnncs50ai2024deep-learningharvardxknowledgebase-agentsminimaxnlpnltkpropositional-logicpythonqlearningregressionscikitlearntensorflowtensorflowkeras
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
CS50's Introduction to Artificial Intelligence with Python
This repository contains my project solutions for CS50's Introduction to Artificial Intelligence with Python offered by Harvard University.
Course Overview
CS50 AI explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, I gained exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning.
Projects
Week 0: Search
  • Degrees - Determines the shortest path between any two actors by choosing a sequence of movies that connects them
  • Tic-Tac-Toe - Implements an AI to play Tic-Tac-Toe optimally using Minimax
Week 1: Knowledge
  • Knights - Solves logic puzzles using propositional logic
  • Minesweeper - AI to play Minesweeper using knowledge-based agents
Week 2: Uncertainty
  • PageRank - Ranks web pages by importance using the PageRank algorithm
  • Heredity - Assesses the likelihood of a person having a genetic trait using Bayesian networks
Week 3: Optimization
  • Crossword - Generates crossword puzzles using constraint satisfaction
Week 4: Learning
  • Shopping - Predicts whether online shopping customers will complete a purchase using k-nearest neighbors
  • Nim - AI that learns to play Nim through reinforcement learning (Q-learning)
Week 5: Neural Networks
  • Traffic - Neural network to identify traffic signs using TensorFlow/Keras
Week 6: Language
  • Parser - Parses sentences and extracts noun phrase chunks
  • Questions - AI to answer questions given a corpus of text using tf-idf and natural language processing
Technologies Used
  • Python
  • scikit-learn
  • TensorFlow/Keras
  • NLTK (Natural Language Toolkit)
  • Tensorflow
Note
This repository contains only my code implementations. Test data and course materials are not included(I don't want to set github servers on fire)
Acknowledgments
Special thanks to Professor David J. Malan, Brian Yu and the rest of the CS50 team at Harvard University for creating this excellent course.
About
No description or website provided.
Topics
aibayesianneuralnetworkcnncs50ai2024deep-learningharvardxknowledgebase-agentsminimaxnlpnltkpropositional-logicpythonqlearningregressionscikitlearntensorflowtensorflowkeras
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
CS50's Introduction to Artificial Intelligence with Python
This repository contains my project solutions for CS50's Introduction to Artificial Intelligence with Python offered by Harvard University.
Course Overview
CS50 AI explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, I gained exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning.
Projects
Week 0: Search
  • Degrees - Determines the shortest path between any two actors by choosing a sequence of movies that connects them
  • Tic-Tac-Toe - Implements an AI to play Tic-Tac-Toe optimally using Minimax
Week 1: Knowledge
  • Knights - Solves logic puzzles using propositional logic
  • Minesweeper - AI to play Minesweeper using knowledge-based agents
Week 2: Uncertainty
  • PageRank - Ranks web pages by importance using the PageRank algorithm
  • Heredity - Assesses the likelihood of a person having a genetic trait using Bayesian networks
Week 3: Optimization
  • Crossword - Generates crossword puzzles using constraint satisfaction
Week 4: Learning
  • Shopping - Predicts whether online shopping customers will complete a purchase using k-nearest neighbors
  • Nim - AI that learns to play Nim through reinforcement learning (Q-learning)
Week 5: Neural Networks
  • Traffic - Neural network to identify traffic signs using TensorFlow/Keras
Week 6: Language
  • Parser - Parses sentences and extracts noun phrase chunks
  • Questions - AI to answer questions given a corpus of text using tf-idf and natural language processing
Technologies Used
  • Python
  • scikit-learn
  • TensorFlow/Keras
  • NLTK (Natural Language Toolkit)
  • Tensorflow
Note
This repository contains only my code implementations. Test data and course materials are not included(I don't want to set github servers on fire)
Acknowledgments
Special thanks to Professor David J. Malan, Brian Yu and the rest of the CS50 team at Harvard University for creating this excellent course.
About
No description or website provided.
Topics
aibayesianneuralnetworkcnncs50ai2024deep-learningharvardxknowledgebase-agentsminimaxnlpnltkpropositional-logicpythonqlearningregressionscikitlearntensorflowtensorflowkeras
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
CS50's Introduction to Artificial Intelligence with Python
This repository contains my project solutions for CS50's Introduction to Artificial Intelligence with Python offered by Harvard University.
Course Overview
CS50 AI explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, I gained exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning.
Projects
Week 0: Search
  • Degrees - Determines the shortest path between any two actors by choosing a sequence of movies that connects them
  • Tic-Tac-Toe - Implements an AI to play Tic-Tac-Toe optimally using Minimax
Week 1: Knowledge
  • Knights - Solves logic puzzles using propositional logic
  • Minesweeper - AI to play Minesweeper using knowledge-based agents
Week 2: Uncertainty
  • PageRank - Ranks web pages by importance using the PageRank algorithm
  • Heredity - Assesses the likelihood of a person having a genetic trait using Bayesian networks
Week 3: Optimization
  • Crossword - Generates crossword puzzles using constraint satisfaction
Week 4: Learning
  • Shopping - Predicts whether online shopping customers will complete a purchase using k-nearest neighbors
  • Nim - AI that learns to play Nim through reinforcement learning (Q-learning)
Week 5: Neural Networks
  • Traffic - Neural network to identify traffic signs using TensorFlow/Keras
Week 6: Language
  • Parser - Parses sentences and extracts noun phrase chunks
  • Questions - AI to answer questions given a corpus of text using tf-idf and natural language processing
Technologies Used
  • Python
  • scikit-learn
  • TensorFlow/Keras
  • NLTK (Natural Language Toolkit)
  • Tensorflow
Note
This repository contains only my code implementations. Test data and course materials are not included(I don't want to set github servers on fire)
Acknowledgments
Special thanks to Professor David J. Malan, Brian Yu and the rest of the CS50 team at Harvard University for creating this excellent course.
About
No description or website provided.
Topics
aibayesianneuralnetworkcnncs50ai2024deep-learningharvardxknowledgebase-agentsminimaxnlpnltkpropositional-logicpythonqlearningregressionscikitlearntensorflowtensorflowkeras
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
CS50's Introduction to Artificial Intelligence with Python
This repository contains my project solutions for CS50's Introduction to Artificial Intelligence with Python offered by Harvard University.
Course Overview
CS50 AI explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, I gained exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning.
Projects
Week 0: Search
  • Degrees - Determines the shortest path between any two actors by choosing a sequence of movies that connects them
  • Tic-Tac-Toe - Implements an AI to play Tic-Tac-Toe optimally using Minimax
Week 1: Knowledge
  • Knights - Solves logic puzzles using propositional logic
  • Minesweeper - AI to play Minesweeper using knowledge-based agents
Week 2: Uncertainty
  • PageRank - Ranks web pages by importance using the PageRank algorithm
  • Heredity - Assesses the likelihood of a person having a genetic trait using Bayesian networks
Week 3: Optimization
  • Crossword - Generates crossword puzzles using constraint satisfaction
Week 4: Learning
  • Shopping - Predicts whether online shopping customers will complete a purchase using k-nearest neighbors
  • Nim - AI that learns to play Nim through reinforcement learning (Q-learning)
Week 5: Neural Networks
  • Traffic - Neural network to identify traffic signs using TensorFlow/Keras
Week 6: Language
  • Parser - Parses sentences and extracts noun phrase chunks
  • Questions - AI to answer questions given a corpus of text using tf-idf and natural language processing
Technologies Used
  • Python
  • scikit-learn
  • TensorFlow/Keras
  • NLTK (Natural Language Toolkit)
  • Tensorflow
Note
This repository contains only my code implementations. Test data and course materials are not included(I don't want to set github servers on fire)
Acknowledgments
Special thanks to Professor David J. Malan, Brian Yu and the rest of the CS50 team at Harvard University for creating this excellent course.
About
No description or website provided.
Topics
aibayesianneuralnetworkcnncs50ai2024deep-learningharvardxknowledgebase-agentsminimaxnlpnltkpropositional-logicpythonqlearningregressionscikitlearntensorflowtensorflowkeras
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
CS50's Introduction to Artificial Intelligence with Python
This repository contains my project solutions for CS50's Introduction to Artificial Intelligence with Python offered by Harvard University.
Course Overview
CS50 AI explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, I gained exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning.
Projects
Week 0: Search
  • Degrees - Determines the shortest path between any two actors by choosing a sequence of movies that connects them
  • Tic-Tac-Toe - Implements an AI to play Tic-Tac-Toe optimally using Minimax
Week 1: Knowledge
  • Knights - Solves logic puzzles using propositional logic
  • Minesweeper - AI to play Minesweeper using knowledge-based agents
Week 2: Uncertainty
  • PageRank - Ranks web pages by importance using the PageRank algorithm
  • Heredity - Assesses the likelihood of a person having a genetic trait using Bayesian networks
Week 3: Optimization
  • Crossword - Generates crossword puzzles using constraint satisfaction
Week 4: Learning
  • Shopping - Predicts whether online shopping customers will complete a purchase using k-nearest neighbors
  • Nim - AI that learns to play Nim through reinforcement learning (Q-learning)
Week 5: Neural Networks
  • Traffic - Neural network to identify traffic signs using TensorFlow/Keras
Week 6: Language
  • Parser - Parses sentences and extracts noun phrase chunks
  • Questions - AI to answer questions given a corpus of text using tf-idf and natural language processing
Technologies Used
  • Python
  • scikit-learn
  • TensorFlow/Keras
  • NLTK (Natural Language Toolkit)
  • Tensorflow
Note
This repository contains only my code implementations. Test data and course materials are not included(I don't want to set github servers on fire)
Acknowledgments
Special thanks to Professor David J. Malan, Brian Yu and the rest of the CS50 team at Harvard University for creating this excellent course.
About
No description or website provided.
Topics
aibayesianneuralnetworkcnncs50ai2024deep-learningharvardxknowledgebase-agentsminimaxnlpnltkpropositional-logicpythonqlearningregressionscikitlearntensorflowtensorflowkeras
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Contributors
Languages