UofU Data Science - VideoAndMovie

Lecture 27: Practical advice for using machine learning

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Lecture 27: Practical advice for using machine learning

Lecture 26: Neural networks (continued)

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Lecture 26: Neural networks (continued)

Lecture 25: Neural networks (continued)

1:20:30

Lecture 25: Neural networks (continued)

Lecture 24b: Neural networks

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Lecture 24b: Neural networks

Lecture 24a: Loss minimization (revisited)

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Lecture 24a: Loss minimization (revisited)

Lecture 23b: Logistic regression

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Lecture 23b: Logistic regression

Lecture 23a: Bayesian learning (continued)

32:40

Lecture 23a: Bayesian learning (continued)

Lecture 22b: Introduction to Bayesian learning

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Lecture 22b: Introduction to Bayesian learning

Lecture 22a: Learning as loss minimization

30:47

Lecture 22a: Learning as loss minimization

Lecture 21: Stochastic Gradient Descent for SVM

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Lecture 21: Stochastic Gradient Descent for SVM

Lecture 20: Practical machine learning tutorial

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Lecture 20: Practical machine learning tutorial

Lecture 19: SVMs (continued)

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Lecture 19: SVMs (continued)

Lecture 18a: Boosting and Ensembles (continued)

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Lecture 18a: Boosting and Ensembles (continued)

Lecture 18b: Support vector machines

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Lecture 18b: Support vector machines

Lecture 17: Boosting

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Lecture 17: Boosting

Lecture 16: VC dimensions (continued)

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Lecture 16: VC dimensions (continued)

Lecture 15: VC dimension

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Lecture 15: VC dimension

Lecture 14: Agnostic learning

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Lecture 14: Agnostic learning

Lecture 13: Learnability Results for Consistent Learners

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Lecture 13: Learnability Results for Consistent Learners

Lecture 12: Occam's Razor for a Consistent Learner

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Lecture 12: Occam's Razor for a Consistent Learner

Lecture 11: Computational Learning Theory

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Lecture 11: Computational Learning Theory

Lecture 10: Least Mean Squares Regression

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Lecture 10: Least Mean Squares Regression

Lecture 9: Perceptron (continued)

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Lecture 9: Perceptron (continued)

Lecture 8b: The Perceptron Algorithm

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Lecture 8b: The Perceptron Algorithm

Lecture 8a: Mistake bound learning (continued)

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Lecture 8a: Mistake bound learning (continued)

Lecture 7: The mistake bound model

1:19:19

Lecture 7: The mistake bound model

Lecture 6b: Quantifying learning algorithms

30:57

Lecture 6b: Quantifying learning algorithms

Lectures 6a: Linear models expressiveness

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Lectures 6a: Linear models expressiveness

Lecture 5b: Linear Models

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Lecture 5b: Linear Models

Lecture 5a: Overfitting

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Lecture 5a: Overfitting