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Machine Learning

by Stanford

This course material is only available in the iTunes U app on iPhone or iPad.

Course Description

This course provides a broad introduction to machine learning and statistical pattern recognition.



Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. 
The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.


Students are expected to have the following background:


- Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program.

- Familiarity with the basic probability theory. (Stat 116 is sufficient but not necessary.)

- Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.)

This Stanford course was taught on campus twice per week in 75 minute lectures for the Stanford Engineering Everywhere Initiative.

For more online learning opportunities, please visit Stanford Online.

Customer Reviews

Complex but awesome course

This is certainly a very math intensive courses, but Andrew Ng is a great professor! This was one of the best classes I took while at Stanford... don't know if it quite does it justice here, but a must take if you have interest in the field.

Its ight

Heck yea borther

Very practical

I really enjoyed the course! The professor taught every aspect in an amusing way! Thanks!

Machine Learning
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Customer Ratings