Quiz on classical supervised learning

In supervised learning the goal is to give a label to an input. *

True

False

We have a data set of 10000 images with labels. To train an algorithm on them we have to. *

Select the algorithm that predicts the label on all images as well as possible.

Separate the data into training and test data and then train the algorithm on the training data.

Separate the data into training and test data and then train the algorithm on the test data.

Logistic regression is enough to predict the labels of any kind of labelled data with high precision. *

True

False

Logistic regression has the following properties: *

It interpolates between between -1 and 1.

The value of the transition from the two labels is set by the bias.

The value of the transition from the two labels is set by the weight.

The sharpness of the transition from the two labels is set by the bias.

The sharpness of the transition from the two labels is set by the weight.

Weight and bias are two parameters that should be optimized during the training.

The accuracy of the algorithm should be identical for test and training set *

True

False

What happens if I did not pass ?

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A beginners guide to quantum machine learning

Quiz on classical supervised learning

What happens if I did not pass ?