20 episodes

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.

Machine Learning Stanford

    • Technology
    • 3.8, 110 Ratings

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

3.8 out of 5
110 Ratings

110 Ratings

MarryC1 ,

awesome course

I took this course and it is the way to get started with fundementals of ML.

Улугбек ,

Thank you so much

Really super course. Thank you prof. Ng and Stanfor U.

cfalt007 ,

Professor Ng is a good lecturer.

Thanks for making available. The lectures are clear and easy to follow as well as a professional audio production.

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