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Called maximum likelihood learning - PowerPoint PPT Presentation

Geoffrey Hinton

CSC2535: Lecture 3: Ways to make backpropagation generalize better, and ways to do without a supervision signal. Geoffrey Hinton. Overfitting. The training data contains information about the regularities in the mapping from input to output. But it also contains noise

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555 views • 54 slides

Geoffrey Hinton

CSC321: 2011 Introduction to Neural Networks and Machine Learning Lecture 10: The Bayesian way to fit models. Geoffrey Hinton. The Bayesian framework. The Bayesian framework assumes that we always have a prior distribution for everything. The prior may be very vague.

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323 views • 13 slides

ICS 178 Introduction Machine Learning & data Mining

ICS 178 Introduction Machine Learning & data Mining. Instructor max Welling Lecture 4: Least squares Regression. What have we done so far?. parametric. non-parametric. density estimation: parzen-windowing. future example: k-mean. unsupervised. classification. regression. regression.

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189 views • 9 slides

ICS 178 Introduction Machine Learning & data Mining

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174 views • 9 slides

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Called maximum likelihood learning - PowerPoint PPT Presentation

Geoffrey Hinton

Geoffrey Hinton

ICS 178 Introduction Machine Learning &amp; data Mining

ICS 178 Introduction Machine Learning &amp; data Mining

ICS 178 Introduction Machine Learning & data Mining

ICS 178 Introduction Machine Learning & data Mining