Difference between ML and AI Flashcards

1
Q

What are the two main applications of Artificial Intelligence (AI) and Machine Learning (ML)

A

Classification and Regression

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2
Q

What is supervised learning

A

When the Ml algorithm learns a function that maps an input to an output based on examples of input-output pairs. e.g learns what is a dog from pictures that are labelled as dogs

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3
Q

What is unsupervised learning

A

When the ML algorithm looks for patterns in a data set with no pre-existing labels or human supervision.

The algorithm learns about data points based on their relationship to other data points.

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4
Q

Can PCA and Cluster analysis be categorised as supervised or unsupervised learning?

A

Unsupervised as we don’t have labelled data, and try to say something about data points in their relationship to others.

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5
Q

What is the difference between unsupervised and supervised learning?

(Range of problems and insights)

A

labaled and unlabeled data

Unsupervised learning is open for a wider range of problems than supervised learning, but the insights we can gain are less powerful.

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6
Q

What are classification problems?

A

It is about labeling data, for example, a classifier that tells us if an image has a bird in it: it finds birst and non-bird images.

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7
Q

What is regression problems

A

Regression is about estimating continuous values, e.g given a set of features about a house, predict its price.

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8
Q

What is clustering problems about

A

Clustering is about a data point’s relation (e.g. distance) to other data points.

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9
Q

The bias-variance dilemma

A

bias-variance problem is the conflict in trying to simultaneously minimize the error in predictions on a training set, causes the model to have issues predicting outside the training set

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