Caltech Machine Learning Lecture 13

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Machine Learning Tutorial Caltech Lecture 13 Validation

Taught by feynman prize winner professor yaser abu-mostafa. the fundamental concepts and techniques are explained in detail. focus of lectures is rea...

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Lecture 13 Validation

Validation - taking a peek out of sample. model selection and data contamination. cross validation. lecture 13 18 caltech's machine learning course c...

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Machine Learning 10 701 Lecture 13

Machine learning 10-701 lecture 13

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Machine Learning Lecture 13 Fall 2016

Machine learning - lecture 13 (fall 2016)

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Machine Learning Lecture 10 Chapter 13 Continued

Machine learning: lecture 10, chapter 13 (continued)

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Machine Learning Fall 2017 Lecture 13

Machine learning - fall 2017 lecture 13

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Lecture 12 of 18 caltech's machine.

Machine learning lecture 13 (fall 2016). Lecture 10, chapter 13 (continued). Lecture 1 18 caltech's m.

The learning problem - introduction; supervised, unsupervised, and
reinforcement learning. components of problem. lecture 1 18 caltech's
m...
The linear model ii - more about models. logistic regression, maximum
likelihood, and gradient descent. lecture 9 of 18 caltech's machine
learning ...
Machine learning: lecture 10, chapter 13 (continued)
Lecture 13 Computational Learning Theory Part 1

Lecture #13 - computational learning theory (part 1)

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Lecture 12 Regularization

Regularization - putting the brakes on fitting noise. hard and soft constraints. augmented error weight decay. lecture 12 of 18 caltech's machine ...

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Lecture 09 The Linear Model Ii

The linear model ii - more about models. logistic regression, maximum likelihood, and gradient descent. lecture 9 of 18 caltech's machine learning ...

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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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Augmented error weight decay. Lecture 9 of 18 caltech's machine learning. The linear model ii more about models.

Supervised, unsupervised, and reinforcement learning. Taught by feynman prize winner professor yaser abumostafa. Lecture 13 18 caltech's machine learning course c.

Logistic regression, maximum likelihood, and gradient descent. The learning problem introduction. Lecture #13 computational learning theory (part 1).

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Caltech Machine Learning Lecture 13