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
    • 3.9, 120 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

3.9 out of 5
120 Ratings

120 Ratings

Felixrpl ,

Inspiring

Awesome and very inspiring lectures to achieve things with the machine learning.

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.

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