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Machine Learning

by Andrew Ng

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Description

This course provides a broad introduction to machine learning and statistical pattern recognition. The course also discusses recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing. 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.

Customer Reviews

Good videos for beginner of machine learning

first, Thanks to Ng.
These are fantastical lectures for learning machine learning course. Recommend every beginners to watch them.
Hope more excellent videos for these research fields. For example, Prof. D. Koller's videos of probability graphical models, and so on.

This is awesome!!

Thx

I'm loving it

A very useful and easy-to-follow course. Thanks to Prof. Ng

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