IGNOU MSCRWEE MCS 224 Artificial Intelligence and Machine Learning Solved Assignment ENGLISH

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MCS 224 (January 2026 - July 2026) - ENGLISH

Course Code: MCS-224

Course Title: Artificial Intelligence and Machine Learning

Assignment Code: MCS-224/TMA/2026

Maximum Marks: 100

Last Date of Submission: April 30, 2026 (For June TEE), September 30, 2026 (For December TEE)

Note:

1. For any question worth 10 marks the word limit is 350 words, for a 20 mark question it is 550 words.

2. Attempt all questions. All questions carry equal marks.

Q.1 Compare ANI, AGI and ASI, in context of AI. Also, discuss the major applications of AI.

Q.2 What is Turing Test? What is the Criticism to the Turing Test?

Q.3 Compare Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)

Q.4 What are Intelligent agents in AI? Briefly discuss the properties of Agents.

Q.5 Discuss the transforming an FOPL Formula into Prenex Normal Form with suitable example. Also, discuss Skolomization with a suitable example.

Q.6 Explain Forward Chaining Systems and Backward Chaining Systems with a suitable example for each.

Q.7 Write Back Propagation algorithm, and showcase its execution on a neural network of your choice. (make suitable assumptions if any)

Q.8 Briefly discuss the various Ensemble methods.

Q.9 For the given points of two classes red and blue:

Blue: { (1, 2), (2,1), (1,-2), (2,-2)}

Red: { (4,-1), (4,1), (5,-1), (6,1)}

Plot a graph for the red and blue categories. Find the support vectors and optimal

separating line.

Q.10 Consider the two-dimensional patterns (2, 2), (3, 6), (4, 4), (5, 6), (6, 7), (7, 8), (8,8) and (9, 10). Using the PCA Algorithm, calculate the primary component.

MCS 224 2025 2026 - English

MCS-224: Artificial Intelligence and Machine Learning

Tutor Marked Assignment

Course Code: MCS 224

Asst. Code: MCS 224/AST/2025-2026

Total Marks: 100

Note: Answer all questions. Question no. 1-4 are of 7 marks each and question no. 5-16 are of 6 marks each. You may use illustrations and diagrams to enhance the explanations.

Q1. What is learning? Define the following ways of learning:

(I) Rote Learning (II) Learning by Instruction (III) Learning by analogy (IV) Learning by

Induction (V) Learning by deduction

Q2. Define is Artificial Intelligence? What are the applications of Al in the healthcare and agricultural domains?

Q3. Find the minimum cost path for the 8-puzzle problem, where the start and goal state are given as follows:

That's an interesting request! The table shown in the image represents a classic 8-puzzle problem, where the goal is to transform the "Start State" into the "Goal State" by sliding the numbered tiles into the empty space.

Here are the tables presented in a text format, mirroring the image:

Start State

1	2	3
4	8	-
7	6	5

Goal State

1	2	3
4	5	6
-	7	8

Q4. Apply BFS algorithm on the following graph.

Q5. Draw a semantic network for the following English statement:

"Shyam owns a dog named Sheru, and Sheru likes to chase cats".

Q6. In a class, three students tossed one coin (one each) 3 times. Answer the following:

(a) Write down all the possible outcomes which can be obtained in this experiment.

(b) What is the probability of getting 2 or more than 2 heads at a time? Also, write the probability of getting three tails at a time.

(c) Calculate the Relative frequency of tail rn(T).

Q7. Explain Dempster-Shafer theory with a suitable example.

Q8. For the following fuzzy sets:

A = {a/0.6, b/0.4, c/0.5, d/0.0, e/0.8}; B = {a/0.2, b/0.8, c/0.7, d/0.3, e/0.5}

C = {a/0.1, b/0.2, c/0.8, d/0.6, e/0.2}

Find the fuzzy sets: (i) A U B UC (ii) A ∩ B ∩ C (iii) A' U B' U C' (iv) A' ∩ B' ∩ C'

(v) (A ∩ B UC)'

Q9. What is ensemble learning? Explain three primary classes of ensemble learning methods.

Q10. Use Naive Bayes classification method for the following dataset and classify the class (Species) of X = {Color=Green, Legs=2, Height=Tall, Smelly=No}

Sl. No.	Color	Legs	Height	Smelly	Species
1	White	3	Short	Yes	M
2	Green	2	Tall	No	M
3	Green	3	Short	Yes	M
4	White	3	Short	Yes	M
5	Green	2	Short	No	H
6	White	2	Tall	No	H
7	White	2	Tall	No	H

Sl. No.	Color	Legs	Height	Smelly	Species
8	White	2	Short	Yes	H

Q11. What is a Decision Tree? Use ID3 algorithm to create the decision tree for the following dataset and use it to find the class of unknown sample X = {Peter, red, short, average}

Name	Hair	Height	Weight	Lotion	Result
Sarah	blonde	average	light	no	sunburned (positive)
Dana	blonde	tall	average	yes	none (negative)
Alex	brown	short	average	yes	none
Annie	blonde	short	average	no	sunburned
Emily	red	average	heavy	no	sunburned
Pete	brown	tall	heavy	no	none
John	brown	average	heavy	no	none
Katie	blonde	short	light	yes	none

Q12. Find a quadratic regression model for the following data. Use the regression model and calculate the value of Y at X = 9.

X	3	4	5	6	7
Y	2.5	3.2	3.8	6.5	11.5

Q13. For the given points of two classes Blue and Yellow:
Blue: { (1,2), (2,3), (-1,2), (-1,4), (-1,-1)}

Yellow: { (4,2), (5,-1), (5,1), (6,1), (5,3)}

Plot a graph for the Blue and Yello categories. Find the support vectors and optimal separating line.

Q14. The following diagram represents a feed-forward neural network with one hidden layer:

MCS 224 (January 2025 - July 2025) - ENGLISH

Course Code : MCS-224

Course Title : Artificial Intelligence and Machine Learning

Assignment Number : MCA_NEW(III)/224/Assign/2025

Maximum Marks : 100

Weightage : 30%

Last date of Submission : 30th April, 2025 (for January session)

31st October, 2025 (for July session)

This assignment has 16 questions of 5 Marks each, answer all questions. Rest 20 marks are for viva voce. Please go through the guidelines regarding assignments given in the Programme Guide for the format of presentation.

Q1: Compare ANI, AGI and ASI, in context of AI. Also, discuss the major applications of AI.

Q2: What is Turing Test? What is the Criticism to the Turing Test?

Q3: Compare Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL).

Q4: What are Intelligent agents in AI? Briefly discuss the properties of Agents.

Q5: Find the minimum cost path for the 8-puzzle problem, where the start and goal state are given as follows:        

 

1	2	8
8		4
7	6	6

   Start State

2	8	1
	4	3
7	6	5

Goal State

Q6: Consider the following Minimax game tree search in which root is maximizing node and children are visited from left to right. Find the value of the root node of the game tree?

Q7: Define a frame for the entity date which consists of day, month and year. each of which is a number with restrictions which are well-known. Also, a procedure named compute-day-of-week is already defined.

Q8: In a class, three students tossed one coins (one each) for 3 times. Write down all the possible outcomes which can be obtained in this experiment. What is the probability of getting 2 or more than 2 heads at a time? Also find the probability of getting three tails at a time.

Q9: Briefly discuss the various Ensemble Methods.

Q10: Explain K-Nearest Neighbour (K-NN) classification algorithm with the help of a suitable example

Q11: Using the following training dataset, apply Naïve Bayes classification algorithm to find the class of an unknown sample X = < Rainy, Cool, High, False >

S. No	Outlook	Temperature	Humidity	Windy	Play Golf/Class
0	Rainy	Hot	High	False	No
1	Rainy	Hot	High	True	No
2	Overcast	Hot	High	False	Yes
3	Sunny	Mild	High	False	Yes
4	Sunny	Cool	Normal	False	Yes
5	Sunny	Cool	Normal	True	No
6	Overcast	Cool	Normal	True	Yes
7	Rainy	Mild	High	False	No
8	Rainy	Cool	Normal	False	Yes
9	Sunny	Mild	Normal	False	Yes
10	Rainy	Mild	Normal	True	Yes
11	Overcast	Mild	High	True	Yes
12	Overcast	Hot	Normal	False	Yes
13	Sunny	Mild	High	True	No

Q12: Explain working of SVM algorithm with the help of a suitable example.

Consider the following set of data points (Year of experience salary). Find the 2^nd order polynomial y=?₀ + ?₁?_i + ?₂ ?_i ² , and using polynomial regression determine the salary when year of experience is 10.

Years of Experience (X)	Salary (Y) in Dollar
1	50,000
2	55,000
3	65,000
4	80,000
5	110,000
6	150,000
7	200,000

Q14: Write Back Propagation algorithm, and showcase its execution on a neural network of your choice (make suitable assumptions if any)

Q15: Consider the two-dimensional patterns (2, 2), (3, 6), (4, 4), (5, 6), (6, 7), (7, 8), (8, 8) and (9, 10). Using the PCA Algorithm, calculate the primary component

Q16: Compute the Linear Discriminant projection for the following two-dimensional dataset X1 = (x1, x2) = {(4,2), (2,1), (2,4), (3,5), (4,5)} and X2 = (x1, x2) = {(9, 9), (6, 9), (9, 6), (8, 7), (10, 9)}

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