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Machine Learning

Total questions: 13

Worksheet time: 7mins

Name
Class
Date
1.

Define supervised learning

a)

Learning that isn't visioned

b)

Unsupervised machine learning task of learning a function that maps an input to an output based on example input-output pairs.

c)

Unsupervised learning algorithms infer patterns from a dataset without reference to known, or labeled, outcomes

d)

Learning that is visioned

2.

Why is reinforcement learning important?

a)

Reinforcement learning delivers decisions. By creating a simulation of an entire business or system, it becomes possible for an intelligent system to test new actions or approaches, change course when failures happen (or negative reinforcement),

b)

Reinforcment learning is important becuase it doesnt deliver decisions to you. It encourages you to make your own decisions.

c)

Reinforcment learning is important because it is used to help the user with their decisions.

3.

Which one of these are benefits from deep learning?

a)

Better Self learning capability

b)

Visioned

c)

Reliability

d)

Feature generation automation

4.

Define which learning is the example. (Linear regression for regression problems.)

a)

Unsupervised learning

b)

Deep learning

c)

Reinforced learning

d)

Supervised learning

5.

Define which learning is the example. (exploratory analysis and dimensionality reduction)

a)

Supervised learning

b)

Reinforced learning

c)

Unsupervised learning

d)

Deep learning

6.

Define the learning from the example. (your cat is an agent that is exposed to the environment)

a)

Reinforcement learning

b)

Deep learning

c)

Supervised learning

d)

Unsupervised learning

7.

Define what learning is the example. (Virtual assistants, vision for driverless cars)

a)

Reinforcement learning

b)

Supervised learning

c)

Deep learning

d)

Unsupervised learning

8.

Define Supervised learning.

a)

category of machine learning and artifical intelligence. It is defined by its use of unlabeled assets to use algorithms that unclassifys data or predicts outcomes unaccurately.

b)

subcategory of machine learning and artificial intelligence. It is defined by its use of labeled datasets to train algorithms that to classify data or predict outcomes accurately.

9.

Define unsupervised learning.

a)

Unsupervised learning algorithm is given an input dataset containing images of different types of cats and dogs.

b)

Unsupervised learning algorithm is given an output dataset containing videos of cats and mouses.

10.

Define predictive analysis

a)

the use of satistics and modeling techniques to make predictions about future outcomes and performance.

b)

The use of analytics and remodeling techniques to make accurate predictions about outcomes in the future.

11.

Define decision tree

a)

decision tree is a branching set of rules used to classify a record, or predict a continuous value for a record.

b)

decision tree is set of branches that has rules set to classify a record, or predit continuous value for a image

12.

Explain how machine training benefits.

a)

Machines can grow human conscicouness. be able to imitate humans and solve problems thatk require human intelligence.

b)

Machines will get used to the training and master the training.

c)

Machines can gain more Artifical Intelligence.

d)

M

13.

What is the definition of supervised learning?

a)

Algorithm that is reserved to output data that has been labeled for a particular output

b)

Supervised learning is an approach to creating artificial intelligence (AI), where a computer algorithm is trained on input data that has been labeled for a particular output.