The best free Stanford AI courses, curated
Some of the best AI courses in the world are taught at Stanford — and much of the material is quietly public. Most people never find it. We've done the digging: below are the flagship AI courses whose slides, notes, assignments or lecture videos you can access for free, organized into a path with our notes on where to start.
These are curated links to publicly available course materials hosted by Stanford University and its instructors. All content belongs to its respective authors. AnybodyCanAI is not affiliated with, sponsored by, or endorsed by Stanford University. The Stanford name is used only to identify the source of each course.
Conversational Virtual Assistants with Deep Learning
Building dependable, non-hallucinating LLM agents
Monica Lam · Fall 2025 · Computer Science
Summary
A project-oriented course on turning LLMs into reliable conversational assistants: retrieval-augmented generation without hallucination, querying databases and knowledge graphs in natural language, agentic pipelines, and formal methods (SMT) for reasoning.
One of the most current, agent-focused courses anywhere — the full lecture deck for every session is public. Ideal if you are building production LLM agents and want Stanford-grade grounding on avoiding hallucination.
Schedule
- 19/22Introduction
- 29/24Knowledge Curation
- 39/29Building a Task-Oriented Agent
- 410/6Evaluation of Task-Oriented Agents
- 510/13Grounding Conversational Agents on Free Text
- 610/22Intro to Agents for Structured and Hybrid Data
- 710/27Structured / Unstructured Query Language (SUQL)
- 810/29Question Answering on Sets of Long Documents
- 911/3Document Set Analysis: Qualitative Coding
- 1011/5Agentic AI for Knowledge Base Queries
- 1111/10Satisfying Natural Language Constraints Using SMT
- 1211/12NLP Building Blocks
- 1311/17Multimodal Applications
- 1411/19Training LLMs
Access
All lecture slide decks are public PDFs on the course server. Lecture videos are on Canvas for enrolled students; the final Project Gallery is public.
Materials & links
Source: Stanford University — all links open on the provider's own site.
Transformers United
Seminar with leading AI researchers
Steven Feng, Karan Singh, Christopher Manning, Div Garg (past) · Spring 2026 (V6) · Computer Science
Summary
A public seminar where frontier researchers explain the latest in transformers — from LLMs to robotics, biology and medicine. Each session is a self-contained talk.
The single most accessible thing on this list: anyone can audit in person or by livestream, and every talk lands on YouTube with millions of views. Perfect 'stay current' viewing that needs no prerequisites — squarely on-mission for AnybodyCanAI.
Schedule
Lecture topics and dates rotate each offering — we link straight to Stanford University's official schedule and syllabus so it's always current.
Full schedule & syllabusAccess
Open to auditors; all talks published publicly on YouTube.
Materials & links
Source: Stanford University — all links open on the provider's own site.
Natural Language Processing with Deep Learning
Diyi Yang, Yejin Choi, Christopher Manning (past) · Winter 2026 · Computer Science
Summary
The definitive academic NLP course: word vectors, RNNs, attention, transformers, pretraining and modern language models, with public slide decks and reading notes.
The canonical foundation behind modern LLMs. Public slides + a full recorded lecture series make it a complete self-study track for the theory under everything you build.
Schedule
Lecture topics and dates rotate each offering — we link straight to Stanford University's official schedule and syllabus so it's always current.
Full schedule & syllabusAccess
Lecture slides and reading notes are public PDFs on the course site; a full video series (Manning, 2023) is public on YouTube. Current-term videos are on Canvas.
Materials & links
Source: Stanford University — all links open on the provider's own site.
Deep Learning for Computer Vision
Fei-Fei Li, Ehsan Adeli, Justin Johnson (past) · Spring 2026 · Computer Science
Summary
The course that taught a generation of practitioners CNNs and deep vision. Famous for its exceptionally clear public lecture notes and hands-on assignments.
The cs231n.github.io notes are a gold-standard, beginner-approachable written intro to neural networks — arguably the best free deep-learning notes on the internet.
Schedule
Lecture topics and dates rotate each offering — we link straight to Stanford University's official schedule and syllabus so it's always current.
Full schedule & syllabusAccess
Course notes and assignments are fully public. Lecture videos from prior years (2017 and 2025) are on YouTube; current-term videos are on Canvas.
Materials & links
Source: Stanford University — all links open on the provider's own site.
Machine Learning
Andrew Ng (2018 recordings), Jehangir Amjad, Anand Avati · Course site current; public video Autumn 2018 · Computer Science
Summary
The rigorous, math-first introduction to machine learning — supervised and unsupervised learning, learning theory and more. Andrew Ng's recorded version is a classic full-course reference.
The deep end of ML fundamentals. If you want to actually understand the math rather than call an API, Ng's full 2018 lecture series is the reference version.
Schedule
Lecture topics and dates rotate each offering — we link straight to Stanford University's official schedule and syllabus so it's always current.
Full schedule & syllabusAccess
Andrew Ng's complete Autumn 2018 lectures are public on YouTube. Current-term course documents are shared with Stanford affiliates only — use the video series for self-study.
Materials & links
Source: Stanford University — all links open on the provider's own site.
Language Modeling from Scratch
Tatsunori Hashimoto, Percy Liang · Spring 2025 · Computer Science
Summary
Build a language model end-to-end: data, tokenization, transformer architecture, training, systems/efficiency and evaluation — with minimal scaffolding.
The most hands-on "understand LLMs by building one" course available. Slides and all five assignments are public on GitHub — a genuine build-it curriculum, free.
Schedule
Lecture topics and dates rotate each offering — we link straight to Stanford University's official schedule and syllabus so it's always current.
Full schedule & syllabusAccess
Lecture slides (and executable lecture code) and all assignments are public. Assignment starter code is on GitHub; a public video series is also available.
Materials & links
Source: Stanford University — all links open on the provider's own site.
Reinforcement Learning
Emma Brunskill · Winter 2026 · Computer Science
Summary
A thorough introduction to reinforcement learning: exploration vs. exploitation, generalization, and applications to robotics, games and healthcare.
The RL grounding behind everything from game-playing agents to the RLHF that aligns modern chatbots. Slides are public on the course site; prior-year lectures are on YouTube.
Schedule
Lecture topics and dates rotate each offering — we link straight to Stanford University's official schedule and syllabus so it's always current.
Full schedule & syllabusAccess
Lecture slides are available from the course website; recorded lectures from prior offerings are on YouTube. Verify current-term links on the site before relying on them.
Materials & links
Source: Stanford University — all links open on the provider's own site.