The best free Cornell AI courses, curated
Cornell is home to some of the most beloved machine-learning teaching online — above all Kilian Weinberger's ML lectures, which many people credit as the course that finally made ML click. Below are Cornell courses whose full videos and notes are free, organized into a path from core ML to a broader applied survey.
These are curated links to publicly available course materials hosted by Cornell University and its instructors (course sites and YouTube). All content belongs to its respective authors. AnybodyCanAI is not affiliated with, sponsored by, or endorsed by Cornell University. The Cornell name is used only to identify the source of each course.
Machine Learning for Intelligent Systems
Kilian Weinberger's classic ML course
Kilian Weinberger · Fall 2018 · Computer Science
Summary
A complete introduction to the core machine-learning algorithms — k-NN, perceptron, naive Bayes, logistic regression, SVMs and kernels, decision trees, bagging and boosting, and neural networks — with the intuition and the math side by side.
Widely regarded as one of the best ML lecture series ever put online: Weinberger's hour lectures are famous for making hard ideas genuinely click. Both the video lectures and the detailed written lecture notes are free.
Schedule
Lecture topics and dates rotate each offering — we link straight to Cornell University's official schedule and syllabus so it's always current.
Full schedule & syllabusAccess
The full YouTube playlist and the written lecture notes are public. This is the archived Fall 2018 offering. A little linear algebra, probability and Python help.
Source: Cornell University — all links open on the provider's own site.
Applied Machine Learning
A broad, practical survey of ML
Volodymyr Kuleshov · Open online course · Cornell Tech
Summary
A broad, applied introduction to machine learning based on Cornell Tech’s CS 5785: supervised and unsupervised learning, regularization, kernels, tree methods, neural networks and probabilistic models — 23 lectures, each with detailed notes and code.
A superb, self-contained survey that pairs 30+ hours of video with polished written notes and runnable notebooks — ideal if you want breadth across the whole ML toolbox with a practical, code-first slant.
Schedule
Lecture topics and dates rotate each offering — we link straight to Cornell University's official schedule and syllabus so it's always current.
Full schedule & syllabusAccess
All 23 lectures are on YouTube with matching notes in HTML, PDF and Jupyter-notebook form on the course website. Free and open.
Materials & links
Source: Cornell University — all links open on the provider's own site.