Free Printable Machine Learning Fundamentals Unit

1.

What is defined as a program that exhibits cognitive ability similar to human beings ?

a)

Artificial Intelligence

b)

Machine Learning

c)

Deep Learning

d)

Data Science

2.

What field of study gives Computers the ability to learn by themselves ?

a)

Big Data Analytics

b)

Statistics

c)

Machine Learning

d)

Data Science

3.

What is Learning ?

a)

Repeating Same Mistakes

b)

Improved Performance with Experiences

c)

Memorization

d)

Forgetting Experiences

4.

How many Types of Machine Learning are there ?

a)

2

b)

1

c)

4

d)

7

5.

What type of Learning uses a Small Dataset ?

a)

Deep Learning

b)

Unsupervised Learning

c)

Supervised Learning

d)

Reinforcement Learning

6.

Which is the correct Example of Supervised Learning ?

a)

Image Classification

b)

Principal Component Analysis

c)

Robotics

d)

Gaming

7.

Name the types of Supervised Learning.

(a)

8.

What type of Learning is used here ?

(a)

9.

Can Supervised Learning Models handle Complex Tasks ?

a)

Yes

b)

No

10.

Can Supervised Learning predict Correct Output if the Test Data is different from the Training Dataset ?

a)

Yes

b)

No

11.

For Unsupervised Learning, is it necessary for the Dataset to be Machine-Readable ?

a)

No

b)

Yes

12.

Name the most popular type of Unsupervised Learning.

(a)

13.

Classify the Objects of the Other Side

(a)

14.

Unsupervised Learning Algorithms can adapt to the data by dynamically changing

(a)

15.

Unsupervised Learning can be used for Complex Tasks.

a)

True

b)

False

16.

Which one would be more accurate ?

a)

Supervised Learning

b)

Unsupervised Learning

17.

Which one would be more difficult ?

a)

b)

18.

Can Supervised and Unsupervised Learning be used together ?

a)

Yes

b)

Not Possible

19.

How many Assumptions are needed for Semi-Supervised Learning ?

(a)

20.

Which Dataset must be Higher for Semi-Supervised Learning ?

a)

Labeled Dataset

b)

Unlabeled Dataset

21.

Reinforcement Learning uses the Method of

(a)

22.

For Reinforcement Learning, Favorable Outcomes are and Unfavorable Outcomes are .

a)

Rewarded and Punished

b)

Trial and Error

c)

Clear and Repeat

d)

Stop and Restart

23.

Select the Types of Reinforcement Learning ?

a)

Positive Reinforcement Learning

b)

Negative Reinforcement Learning

c)

Neutral Reinforcement Learning

24.

Throw a die. What is the probability of getting a 3 ?

a)

16.7%

b)

1.67%

c)

0.167%

d)

32%

25.

Name of this Equation : $P\left(B\left|A\right|\right)\ =\ \frac{P\left(A\left|B\right|\right)P\left(B\right)}{P\left(A\right)}$

(a)

26.

Two vectors are given by, $a\ =\ 2i\ +\ j\ +k$ and $b\ =\ i\ +\ j\ +\ k$ . Find the cross product of these two vectors.

a)

$-\ j\ +\ k$

b)

$-\ j\ -\ k$

c)

$j\ +\ k$

d)

$j\ -\ k$

27.

Choose the Correct Product of the Given Matrices.

a)

b)

c)

d)

28.

Find $A^{-1}$ For the Given Matrix A and it's Cofactor.

a)

b)

c)

d)

29.

A bag I contains 4 white and 6 black balls while another Bag II contains 4 white and 3 black balls. One ball is drawn at random from one of the bags, and it is found to be black. Find the 8 probability that it was drawn from Bag I.

(Use Bayes’ Theorem)

a)

$\frac{6}{12}$

b)

$\frac{18}{34}$

c)

$\frac{\ 7}{12}$

d)

$\frac{4}{13}$

30.

Name of this Equation : P( B | A )

(a)

Machine Learning Fundamentals Unit - 1

Total questions: 30

Worksheet time: 44mins

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