Harvard CS50's Introduction to Artificial Intelligence with Python

📌 What is this course?

Part of Harvard University’s celebrated CS50 computer science series, CS50 AI explores the core foundational concepts and algorithms at the foundation of modern artificial intelligence.

Taught by David J. Malan and Brian Yu, this course dives beyond superficial library calls. Students build AI engines from scratch in Python—including adversarial game trees (Minimax/Alpha-Beta pruning) for Tic-Tac-Toe, pathfinding with A search, Bayesian probabilistic inference, constraint satisfaction, and convolutional neural networks with TensorFlow*.

All video lectures, slides, comprehensive text notes, and autograded projects are 100% free to audit for anyone worldwide.

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🎯 Who is this for?

• Programmers with basic Python knowledge who want to understand how AI algorithms work under the hood.
• Learners seeking authentic computer science problem-solving skills rather than commercial bootcamp fluff.
• Anyone eager to experience Harvard’s actual undergraduate AI curriculum and projects at zero cost.

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📚 Core Syllabus

1. Search: DFS, BFS, Greedy Best-First, A* Search, Minimax & Alpha-Beta Pruning
2. Knowledge: Propositional Logic, Model Checking, Inference Engines, Resolution
3. Uncertainty: Probability, Conditional Independence, Bayes’ Rule, Markov Models
4. Optimization: Local Search, Hill Climbing, Simulated Annealing, Linear Programming
5. Learning: Supervised Learning, Nearest-Neighbor, Perceptron, SVM, Overfitting Prevention
6. Neural Networks: Artificial Neural Networks, Backpropagation, Computer Vision with TensorFlow
7. Language: Syntax, Semantics, Word Embeddings, Transformers, Natural Language Processing