A week-by-week reading of Stanford CS124: language models, text classification, information extraction, question answering, speech, and the full NLP pipeline.
CS124 is the first course in Stanford's NLP branch. Its textbook is Jurafsky's own Speech and Language Processing, free online, and all nine assignment repos are public. But a banner sits on the course homepage: it will not be taught at all in AY 2026–27. And the chapter numbers the syllabus points at no longer match the August 2026 textbook.
CS124 Winter 2026 opens by mapping a ten-week path from tokenization and classification to retrieval, speech, networks, and LLMs, while PA0 establishes the Jupyter environment used throughout the quarter.
Week 2 builds three layers: a token vocabulary with BPE, sequence comparison with dynamic-programming edit distance, and probability approximation with n-grams; PA1 turns regex and BPE into executable work.
Week 3 connects text features, sigmoid probabilities, cross-entropy loss, and gradient descent, producing a classifier whose feature contributions remain inspectable.
Week 4 builds candidates with an inverted index, ranks them with tf-idf and cosine similarity, and then connects retrieved evidence to generation; PA3 exposes RAG's inspectable retrieval half.
Week 5's public materials support the distributional hypothesis, word embeddings, and cosine similarity; the paired Social NLP lecture is unrecorded and restricted, so concrete audit methods are labeled as author extensions.
Week 6 uses public neural-network slides for weighted sums, nonlinearities, loss, and backpropagation, then a public LLM/Transformer deck labeled 2025 for decoder-only architecture without treating it as the 2026 live transcript.
Week 7's public path is PA6a: implement causal self-attention, train a small Shakespeare Transformer, sample text, and compute perplexity; the live speech lecture remains an explicit source gap.
Week 8 sends text through TTS and back through STT, requiring error classification, formatting-loss analysis, and accent stress tests, while Lab 4 prepares Git collaboration for the team agent project.
Week 9 builds movie recommendations with item-item collaborative filtering, then packages recommendation, web search, databases, and memory as agent tools under API-budget and team constraints.
Week 10 models the Web with anchor text, PageRank, and centrality; post-training, multilinguality, and speech belong only to a public final-deck outline labeled 2025, not the 2026 live narration.