The best free Caltech AI courses, curated
Caltech's Learning From Data is one of the most rigorous free machine-learning courses ever put online — the course many people point to when they want to truly understand *why* learning works, not just how to call a library. Below is the full course, organized with our notes on how to get the most from it.
These are curated links to publicly available course materials hosted by Caltech and Professor Yaser Abu-Mostafa (work.caltech.edu and YouTube). All content belongs to its respective authors. AnybodyCanAI is not affiliated with, sponsored by, or endorsed by Caltech. The Caltech name is used only to identify the source of the course.
Learning From Data
The theory and practice of machine learning
Yaser Abu-Mostafa · 18 lectures · Computing + Mathematical Sciences
Summary
A complete introductory machine-learning course that balances theory, technique and application: the learning problem and whether learning is even feasible, the VC dimension and generalization, the bias-variance tradeoff, linear and neural models, overfitting and regularization, validation, and support vector machines with kernels.
Famous for actually explaining *why* machine learning works. Abu-Mostafa’s lectures are unusually clear on the theory that most practical courses skip — the ideal course if you want depth and real understanding rather than just recipes.
Schedule
Lecture topics and dates rotate each offering — we link straight to California Institute of Technology's official schedule and syllabus so it's always current.
Full schedule & syllabusAccess
All 18 lecture videos (with Q&A), slides, homework sets and the final exam are free on the course site and YouTube. The companion textbook is sold separately. Basic probability, linear algebra and calculus help.
Source: California Institute of Technology — all links open on the provider's own site.