Stanford University · Course
Machine Learning (CS229), Stanford Online
Machine Learning (CS229) on Stanford Online is a broad graduate introduction to machine learning and statistical pattern recognition: supervised and unsupervised learning, learning theory, and reinforcement learning and control.
Who it suits
- People who want a rigorous, mathematical grounding in machine learning.
- Stanford advises reviewing the first problem set before enrolling: if it looks too challenging, the course may be too difficult.
What it covers
- Supervised and unsupervised learning.
- Learning theory.
- Reinforcement learning and control.
- Recent applications of machine learning, and designing and developing algorithms for machines.
Watch here
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Stanford Online · 2023
Stanford CS229 Machine Learning I Introduction I 2022 I Lecture 1
Before you start
- Stanford lists a conferred bachelor’s degree with an undergraduate GPA of 3.0 or better.
- Ability to write a non-trivial program in Python and NumPy.
- Probability theory, and multivariable calculus and linear algebra, or equivalents.
Common questions about Machine Learning (CS229), Stanford Online
- How long is it?
- Stanford lists 8 weeks at 15 to 25 hours a week, and 4 academic units.
- How hard is it?
- Stanford suggests trying the first problem set before enrolling to judge whether the course is too difficult.
- Can I enrol any time?
- Enrolment is only open during set periods, and an application is needed for graduate courses, so check the page for the current dates.
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Where this comes from
Written from Stanford University’s own pages, read on 2026-10-13. Content, prices and availability change, so the provider’s page is the source. We are not affiliated with Stanford University and this is not an endorsement.