The best free Berkeley AI courses, curated
UC Berkeley runs some of the most respected AI, machine-learning and data-science courses anywhere, and much of it — lecture slides, notes, the famous Pac-Man projects, and full video series — is public. Below are the standouts, from classic AI through deep reinforcement learning, with links straight to Berkeley.
These are curated links to publicly available course materials hosted by UC Berkeley and its instructors. All content belongs to its respective authors. AnybodyCanAI is not affiliated with, sponsored by, or endorsed by UC Berkeley. The Berkeley name is used only to identify the source of each course.
Introduction to Artificial Intelligence
Home of the legendary Pac-Man projects
UC Berkeley EECS · Course site rolls forward each term · EECS
Summary
A broad introduction to AI: search, constraint satisfaction, adversarial games, Markov decision processes, reinforcement learning and probabilistic reasoning — anchored by the much-loved Pac-Man programming projects.
One of the best-designed intro-AI courses anywhere. The public slides and Pac-Man projects are a genuinely fun, hands-on way to learn the classical foundations of AI.
Schedule
Lecture topics and dates rotate each offering — we link straight to UC Berkeley's official schedule and syllabus so it's always current.
Full schedule & syllabusAccess
Lecture slides and the Pac-Man projects are public on the current-term course site; recorded lectures from recent offerings are on YouTube.
Materials & links
Source: UC Berkeley — all links open on the provider's own site.
Deep Reinforcement Learning
Sergey Levine’s graduate deep RL course
Sergey Levine · Spring 2026 (videos: Fall 2023) · EECS
Summary
A rigorous graduate course on deep reinforcement learning: policy gradients, Q-learning, actor-critic methods, model-based RL, exploration and offline RL, with full lecture slides and homeworks.
The reference deep-RL course. Slides and homeworks are public on the course site, and a complete recorded lecture series is on YouTube — a full self-study track.
Schedule
Lecture topics and dates rotate each offering — we link straight to UC Berkeley's official schedule and syllabus so it's always current.
Full schedule & syllabusAccess
Lecture slides and homeworks are public on the course site; the Fall 2023 lecture recordings are on YouTube.
Materials & links
Source: UC Berkeley — all links open on the provider's own site.
Introduction to Machine Learning
Jonathan Shewchuk’s course + famous notes
Jonathan Shewchuk · Course site rolls forward each term · EECS
Summary
A theory-forward introduction to machine learning — classification, regression, SVMs, decision trees, neural networks and unsupervised learning — famous for its clear, comprehensive lecture notes.
Shewchuk’s complete lecture notes are a beloved, rigorous written reference for the math behind ML — one of the best free ML note sets on the internet.
Schedule
Lecture topics and dates rotate each offering — we link straight to UC Berkeley's official schedule and syllabus so it's always current.
Full schedule & syllabusAccess
The course site and the complete lecture-notes PDF are public; recorded lectures from prior offerings are linked from the site.
Materials & links
Source: UC Berkeley — all links open on the provider's own site.
Full Stack Deep Learning
Building and shipping ML-powered products
Josh Tobin, Sergey Karayev, Charles Frye · 2022 edition · Berkeley (origin)
Summary
The practical side of ML that courses usually skip: turning models into real products — data management, training infrastructure, testing, deployment, monitoring and building with LLMs.
Started as a Berkeley bootcamp and is now free online. The perfect complement to theory courses — it teaches how to actually ship ML systems.
Schedule
Lecture topics and dates rotate each offering — we link straight to UC Berkeley's official schedule and syllabus so it's always current.
Full schedule & syllabusAccess
Lectures and labs are free on the course site and on YouTube.
Materials & links
Source: UC Berkeley — all links open on the provider's own site.
Principles and Techniques of Data Science
The data foundations under machine learning
UC Berkeley · Course site rolls forward each term · Data Science
Summary
Berkeley’s upper-division data-science course: working with data in pandas, visualization, SQL, sampling and inference, and regression and classification — the data groundwork every ML practitioner needs.
ML is only as good as your data skills. Data 100’s public materials are a thorough, practical grounding in the data work that surrounds every model.
Schedule
Lecture topics and dates rotate each offering — we link straight to UC Berkeley's official schedule and syllabus so it's always current.
Full schedule & syllabusAccess
Lecture slides, notes and assignments are public on the course site.
Materials & links
Source: UC Berkeley — all links open on the provider's own site.