Machine Learning Flashcards by James Tucker | Brainscape

Q

What is accuracy?

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Q

Describe AdaBoost

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Q

What is adjusted R^2

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Q

What is agglomerative clustering?

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Q

What is AIC?

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Q

Describe the “almost everywhere” phenomenon

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Q

What is Alpha in Ridge Regression?

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Q

What is Anscombe’s Quartet?

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Q

Describe the architecture of a neural network

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Q

Describe Area under the Curve

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Q

How can you avoid over-fitting a model?

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Q

What is back-proprogation and how does it work?

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Q

Describe bag-of-words

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Q

Compare bagging versus dropout

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Q

What is bagging?

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Q

Describe the basics of deep learning

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Q

Define Bayes’ error

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Q

Describe Bayes’ theorem mathematically

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Q

What are the pros and cons of Bayes’ theorem?

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Q

What is bias?

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Q

What is the bias-variance tradeoff?

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Q

What is Big-O notation?

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Q

What is boosting?

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Q

What is bootstrapping?

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Q

Describe the brierscore

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Q

What is capacity in a ML context?

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Q

What is a categorical feature?

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Q

What is the chain rule in calculus?

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Q

How is Chi-squared used in feature selection?

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Q

What is Chi-squared?

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Q

Describe classification

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Q

Describe how you can combine items

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Q

What are some common optimizers for neural networks?

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Q

What are some of the common output layer functions in ML?

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Q

What are concave and convex functions?

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Q

Describe conditional probability

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Q

What is conditioning?

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