Difference between ML and AI

Question 1

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

Answer 1

Classification and Regression

Question 2

What is supervised learning

Answer 2

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

Question 3

What is unsupervised learning

Answer 3

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.

Question 4

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

Answer 4

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

Question 5

What is the difference between unsupervised and supervised learning?

(Range of problems and insights)

Answer 5

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.

Question 6

What are classification problems?

Answer 6

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.

Question 7

What is regression problems

Answer 7

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

Question 8

What is clustering problems about

Answer 8

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

Question 9

The bias-variance dilemma

Answer 9

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