A lecture-by-lecture reading of Harvard CS50’s AI with Python: search, knowledge representation, probability, machine learning, neural networks, and language, based on the public course materials and assignments.
CS50 AI's OpenCourseWare edition publishes seven weeks of lectures, slides, notes, and twelve Python projects with autograder feedback, plus a free CS50 Certificate if you score at least 70% on every project. The catch: weeks 0–5 still use the Spring 2020 recordings; only Week 6 (Language) was re-recorded, in 2023.
Week 0 opens with search algorithms: BFS for shortest paths, Minimax for adversarial play, Alpha-Beta for pruning. Two projects: Degrees (BFS) and Tic-Tac-Toe (Minimax).
Week 1 shifts to knowledge representation: propositional logic syntax, model checking, Modus Ponens/Resolution inference, CNF conversion. Projects: Knights (logic puzzles) and Minesweeper (probabilistic inference).
Week 2 shifts from deterministic to probabilistic: Bayes rule, Bayesian nets with D-separation, Markov chains, PageRank random walks. Projects: Heredity (genotype inference) and PageRank (web ranking).
Week 3 tackles optimization: hill climbing, simulated annealing escaping local optima, CSP framework with AC-3 arc consistency, backtracking with MRV/degree heuristics. Project Crossword builds a crossword puzzle generator.
Week 4 enters ML: supervised classification (k-NN, SVM, Perceptron), model evaluation, RL basics (MDP, Q-learning, ε-greedy). Projects: Shopping (purchase prediction with k-NN) and Nim (learning to play via Q-learning).
Week 5 enters deep learning: perceptron to multi-layer nets, backprop chain rule, loss functions, optimizers, TensorFlow/Keras modeling, CNN conv/pool. Project Traffic trains CNN to classify traffic signs.
Week 6 processes natural language: N-gram conditional probability & smoothing, CFG syntax parsing with CYK, TF-IDF vector retrieval, attention mechanism & Transformer basics. Projects: Parser (syntactic generation) and Questions (TF-IDF QA system).
Synthesis 1: Tracing how seven weeks form a deliberate knowledge arc from symbolic search to language models, revealing the design philosophy from classical AI to modern ML.
Synthesis 2: Complete comparison of 12 projects — core algorithms, LOC estimates, difficulty tiers, check50 acceptance criteria, transferable skills. With difficulty grading and learning sequence advice.
Series finale: Retrospecting timeless core from 7 weeks/12 projects, gaps in 2020/2023 recordings vs 2026 reality, free OCW route completeness, and forward roadmap (Transformers, LLM fine-tuning, RAG, Agents, Evaluation).