Adaptive Contro

Machine Learning

Collection: 
openAcademy
Author: 
Andrew Ng
Year: 
2014
Conditions of Use: 
Attribution-NonCommercial-ShareAlike 3.0 Unported
Media Format: 
Material Type: 
Description: 
This course is offered by Stanford as an online course for credit. It can be taken individually, or as part of a master’s degree or graduate certificate earned online through the Stanford Center for Professional Development.

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.
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