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Machine Learning Yaser Abu-mostafa

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Lecture 01 The Learning Problem

The learning problem - introduction; supervised, unsupervised, and reinforcement learning. components of problem. lecture 1 18 caltech's m...

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Lecture 02 Is Learning Feasible

Is learning feasible? - can we generalize from a limited sample to the entire space? relationship between in-sample and out-of-sample. lecture 2 of 18 cal...

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Lecture 05 Training Versus Testing

Training versus testing - the difference between and in mathematical terms. what makes a learning model able to generalize? lecture 5 of 18 ...

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Lecture 04 Error And Noise

Error and noise - the principled choice of measures. what happens when target we want to learn is noisy. lecture 4 18 caltech's machine learn...

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Lecture 03 The Linear Model I

The linear model i - classification and regression. extending models through nonlinear transforms. lecture 3 of 18 caltech's machine ...

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New World Caltech S Machine Learning By Professor Yaser Abu Mostafa Lecture 4

Error and noise - the principled choice of measures. what happens when target we want to learn is noisy. lecture 4 18 hameetman auditorium at ca...

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Brief views bayesian learning and aggregation methods.

Lecture 2 of 18 cal. Error and noise the principled choice of measures. Lecture 6 18 o.

Epilogue - the map of machine learning. brief views bayesian learning and aggregation methods. lecture 18 caltech's course cs ...
The learning problem - introduction; supervised, unsupervised, and reinforcement learning. components of problem. lecture 1 18 caltech's m...
The linear model i - classification and regression. extending models through nonlinear transforms. lecture 3 of 18 caltech's machine ...
Lecture 18 Epilogue

Epilogue - the map of machine learning. brief views bayesian learning and aggregation methods. lecture 18 caltech's course cs ...

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Edx Caltechx Learning From Data Cs1156x About Video

Learning from data introductory machine course covering theory, algorithms and applications. our focus is on real understanding, not just "knowing."...

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Lecture 06 Theory Of Generalization

Theory of generalization - how an infinite model can learn from a finite sample. the most important theoretical result in machine learning. lecture 6 18 o...

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Lecture 08 Bias Variance Tradeoff

Bias-variance tradeoff - breaking down the learning performance into competing quantities. curves. lecture 8 of 18 caltech's machine learning...

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Lecture 8 of 18 caltech's machine learning. What makes a learning model able to generalize. Lecture 1 18 caltech's m.

Learning from data introductory machine course covering theory, algorithms and applications. Can we generalize from a limited sample to the entire space. Error and noise the principled choice of measures.

Biasvariance tradeoff breaking down the learning performance into competing quantities. Supervised, unsupervised, and reinforcement learning. Training versus testing the difference between and in mathematical terms.

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Machine Learning Yaser Abu-mostafa