VELS INSTITUTE OF SCIENCE, TECHNOLOGY AND ADVANCED

STUDIES
SCHOOL OF ENGINEERING

DEPARTMENT: Computer Science Engineering – (AI/ML)

SEMESTER: IIIrd Semester
TITLE OF THE PAPER: Foundation of AI/ML
SUBJECT CODE: 21CBA135
TIME: 03 Hours Max. Marks: 100
SECTION – A (2 MARKS)
S.No Questions Unit K- CO PO
1.
Level
2.
State your understanding about Intelligence. I 1 1
3.
Define Artificial Intelligence. I 1 1
4.
What is an objective of Artificial intelligence? I 1 1
5.
What do you understand by intelligent agent? I 1 1
6.
Define table-based agents. I 1 1
7.
Define percept-based agents. I 2 1
8.
State goal-based agent. I 2 1
9.
List few of the task domains of AI. I 2 1
10.
What is a Knowledge Based System? I 2 1
11.
What is future of AI? I 2 1
12.
Define Data, Information and Knowledge. I 3 1
13.
How Knowledge is different from Information. I 3 1
14.
What is recommendation system? I 3 1
15.
How antivirus on a system work? I 3 1
16.
How system take decision? I 3 1
Define state space search algorithm. II 1 2
17.

18.
Brief about iterative deepening. II 1 2
19.
Define heuristic search algorithm. II 1 2
20.
State heuristic function. II 1 2
21.
What is breadth first search algorithm? II 1 2
22.
Define best first search. II 2 2
23.
What do you understand by Hill Climbing algorithm? II 2 2
24.
Define variable Neighbourhood descent algorithm. II 2 2
25.
What do you mean by optimal search? II 2 3
26.
Define A* algorithm. II 2 2
27.
What is recursive best first search algorithm. II 3 2
28.
Define depth bounded DFS. II 3 2
29.
Define informed search algorithm. II 3 2
What is uninformed blind search? II 3 2
30.

In how many ways Search algorithm is classified. II 3 2

31.

32.
Define Machine Learning. III 1 3
33.
Define Deep learning. III 1 3
How deep learning is different from machine learning. III 1 3
34.

Define the term Representation, Evaluation and Optimization III 1 3

35.
in terms of Machine learning.
36.
What do you understand by data format? III 1 3
37.
What are the categorical data? III 2 3
What are the numerical data? III 2 3
38.

39.
Define Supervised learning. III 2 3
40.
Define Unsupervised learning. III 2 3
41.
Define reinforcement learning. III 2 3
Define Mean, Mode, Median. III 3 3
42.

What are the different statistical methods were used in III 3 3

43.
machine learning?
Define big data. III 3 3
44.

Define time series data. III 3 3

45.

Define measure of spread in terms of range, percentile and III 3 3

46.
quartiles.
47.
Define feature selection. IV 1 4
48.
Define training set for feature selection. IV 1 4
49.
Define test set during feature selection. IV 1 4
50.
Brief about the missing feature in data set. IV 1 4
What is data scaling? IV 1 4
51.

52.
What is normalization in machine learning? IV 2 4
State filtering process in machine learning. IV 2 4
53.

54.
Define PCA. IV 2 4
55.
Define Sparse PCA. IV 2 4
56.
Define Kernal PCA. IV 2 4
State atom extraction. IV 3 4
57.

58.
State dictionary learning in machine learning. IV 3 4
State non negative matrix factorization. IV 3 4
59.

60.
Define principal component analysis. IV 3 4
61.
What is Sci-kit learn? IV 3 4
Define regression. V 1 5
62.

63.
Define linear regression. V 1 5
64.
State bidirectional modal in Machine learning model. V 1 5
What do you understand by neural network? V 1 5
65.

66.
Define logistic regression. V 1 5
State linear classifier. V 2 5
67.

68.
What do you understand by optimization in machine learning. V 2 5
Define gradient descent. V 2 5
69.

Define batch descent gradient descent. V 2 5

70.

Define stochastic gradient descent. V 2 5

71.

72.
Define mini batch gradient descent. V 3 5
73.
State Bayes Theorem. V 3 5
74.
State Naïve bayes Classifier. V 3 5
Define support vector regression. V 3 5
75.
Define gaussian theorem. V 3 5

SECTION – B (16 MARKS)

S.No Questions Unit K- CO PO
1.
Level
Describe the four categories under which AI is classified with? I 3 1
2.

Write History of Artificial Intelligence. How evolution in I 3 1

3.
technology performed?
What are the future objective of Artificial Intelligence? I 3 1
4.

How artificial intelligence will sort out the real-life problems. I 3 1

5.
Explain by taking case study.
Define Artificial intelligence with its application. I 4 1
6.

To whom you consider an Intelligent? How Human I 4 1

intelligence is different from artificial intelligence.
7.

What are the different intelligent agents were considered in I 4 1

AI? Elaborate it.
8.

Write a detailed note on Table based I 4 1

9.
Agent, Percept based Agent, Goal-based Agent.
What is the criticism aspect of Artificial intelligence? I 5 1
10.

How artificial intelligence will be helpful in Banking, Medical, I 5 1

Sports and Robotics.
11.

Corelate data, information and Knowledge and explain how I 5 1

decisions were taken?
12.

State the concept of AI with its applications. What are the I 5 1

different types of AI Agents.
13.

How revolution in artificial intelligence will change the I 3/4/5 1

technology world?
14.

What is AI technology? How this technology has impacted the I 3/4/5 1

E Commerce industry?
15.

Explain the different type of artificial intelligence-based I 3/4/5 1

16.
agents.
17.
Explain in detail about the concept of State space algorithm. II 3 2
Explain in detail about iterative deepening? II 3 2
18.

What do you understand by heuristic search and heuristic II 3 2

19.
function? Explain.
20.
Write a short note on Best first search concept. II 3 2
21.
Explain the concept of Hill Climbing algorithm. II 4 2
Explain about variable neighbourhood descent algorithm. II 4 2
22.

What do you understand by optimal search? II 4 2

23.

24.
What is A* algorithm? Explain. II 4 2
What are the steps involved in best first search algorithm? II 5 3
25.

Write an algorithm on Hill Climbing concept, with its II 5 2

26.
features.
Explain the concept and algorithm of A* algorithm. II 5 2
27.

Write a short note on Depth First search with example. Also II 5 2

28.
explain its advantage and disadvantages.
Brief about different blind search algorithm, II 3/4/5 2
29.

What do you understand by A* algorithm? Explain its II 3/4/5 2

30.
advantage and disadvantages?
31.
Brief about informed search algorithm. II 3/4/5 2
Explain in detail about the concept of Machine Learning. III 3 3
32.

What do you understand by deep learning? How it is different III 3 3

33.
from Machine learning.
34.
Write a comparative study of AI, ML and DL. III 3 3
Write a comparative study of Big data and Machine learning. III 3 3
35.

What do you understand by Big Data? Explain about three V’s III 4 3
36.
involved in big data.
Explain about biological neural network with diagram. III 4 3
37.

Compare the artificial neural network with biological neural III 4 3

network.
38.

Write your understanding about Supervised and Unsupervised III 4 3

algorithms in machine learning.
39.

What are the different approaches of learnability in machine III 5 3

learning?
40.

What are the different statistical methods were used for III 5 3
machine learning?
41.

Explain the statistical concept of Mean, Mode, Median, III 5 3

Standard deviation and Variance.
42.

Explain the statistical concept to measure of spread in terms of III 5 3

range, percentile and quartile.
43.

Write a short note on normal distribution and probability III 3/4/5 3

44.
distribution function.
What are the key elements of machine learning? Explain. III 3/4/5 3
45.

Write a short note on Numerical data, categorical data, time III 3/4/5 3
series data and textual data.
46.

What do you understand by feature selection? How feature IV 3 4

selection will help in machine learning?
47.

Which library is used in python for machine learning? What IV 3 4

are its features?
48.

Why we do feature selection on dataset? For feature selection IV 3 4

49.
which library is preferred?
50.
What are training and test set in data set? Explain. IV 3 4
What are categorical data? Why do we need to have encode IV 4 4
 them?
51.

What are categorical data? In how many ways categorical data IV 4 4

is divided?
52.

What are missing data in data set? How can we handle missing IV 4 4
data?
53.

What do you understand by Scaling of data set? Why is it IV 4 4

important?
54.

What do you understand by Scaling and normalization of data IV 5 4

55.
set?
What are the different feature selection techniques? Explain. IV 5 4
56.

In how many ways supervised feature selection techniques IV 5 4

were classified?
57.

Explain the concept of information gain, Chi Square Test and IV 5 4

58.
Fisher Score?
59.
Explain the concept of Principal Component Analysis. IV 3/4/5 4
Explain the concept of Kernel PCA , Sparse PCA and NMF. IV 3/4/5 4
60.

Explain the concept of atom extraction and dictionary IV 3/4/5 4

learning.
61.

Explain the concept of regression in machine learning V 3 5

62.
algorithm.
63.
In how ways regression is classified? Explain brief about it. V 3 5
Explain the concept of linear regression in machine learning? V 3 5
64.

65.
Explain the concept of logistic regression in machine learning. V 3 5
Explain the concept of linear classifier in machine learning. V 4 5
66.

Explain the concept of optimization in machine learning. How V 4 5

67.
optimization will be helpful?
68.
Explain the concept of Gradient Descent in machine learning. V 4 5
What are the different type of gradient descent? V 4 5
69.

Explain the concept of Batch Gradient descent, Stochastic V 5 5

70.
gradient descent and mini batch gradient descent.
Explain the concept of ROC Curve. V 5 5
71.

Explain the mathematical concept of Naïve bayes V 5 5

72.
mathematical model.
73.
In how many ways naïve bayes concept is classified? Explain. V 5 5
74.
Explain the concept of Support Vector Machine. V 3/4/5 5
Explain the concept of linear and kernel-based algorithm. V 3/4/5 5
75.
Explain the concept of support vector regression. V 3/4/5 5