DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
Model Examination May - 2024
SET I
SUBJECT CODE/NAME : CS3491- Artificial Intelligence and Machine Learning DATE :
YEAR & SEMESTER : II,IV TIME :
ACADEMIC YEAR : 2023 – 2024 MARKS : 100
CO1: Use appropriate search algorithms for problem solving
CO2: Apply reasoning under uncertainty
CO3: Build supervised learning models
CO4: Build ensembling and unsupervised models
CO5: Build deep learning neural network models
BLOOM'S TAXONOMY
Remembering Applying Evaluating
Understanding Analyzing Creating
PART A (10 x 2 = 20 Marks)
CO1 An 1. Compare Informed and Uninformed Search. 2
CO1 R 2. What is adversarial search? [A/M 2023] 2
CO2 An 3. Given that P(A)=0.3,P(A|B)=0.4 and P(B)=0.5, Compute P(B|A). 2
CO2 R 4. What is Bayesian Belief Network? 2
CO3 An 5. What is the main key difference between supervised and unsupervised machine 2
learning? [A/M 2023]
CO3 R 6. What is a random forest? [A/M 2023] 2
CO4 U 7. What is the significance of Gaussian mixture model? [A/M 2023] 2
CO4 An 8. What is the difference between K-means and KNN? 2
CO5 U 9. What are the advantages of Multilayer Perceptron? 2
CO5 R 10. Name any two activation functions. [A/M 2023] 2
PART B (5 x 13 = 65 Marks)
CO1 An 11.a State the constraint satisfaction problem. Outline local search for constraint 13
satisfaction problem with an example. [A/M 2023]
OR
CO1 U 11.b Outline the uniformed search strategies like breadth-first search and depth-first 13
search with examples. [A/M 2023]
CO2 R 12.a i. What do you mean by inference in Bayesian networks? Outline inference by 13
enumeration with an example. [A/M 2023] (7)
ii. Elaborate on unconditional probability and conditional probability with an
example. [A/M 2023] (6)
OR
CO2 U 12.b Explain in detail about causal networks. 13
CO3 U 13.a Describe the general procedure of random forest algorithm. [N/D 2023] 13
OR
CO3 U 13.b Elaborate on logistics regression with an example. Explain the process of 13
computing coefficients. [A/M 2023]
CO4 An 14.a Outline the steps in the Ada Boost algorithm with an example. [A/M 2023] 13
OR
CO4 U 14.b Elaborate on the steps in expectation maximization algorithm. [A/M 2023] 13
CO5 U 15.a Elaborate the process of training hidden layers by RELU in deep networks. [N/D 13
2023]
OR
CO5 R 15.b Draw the architecture of a Multilayer perceptron (MLP) and explain its operation. 13
Mention its advantages and disadvantages.
PART C (1 x 15 = 15 Marks)
CO1 An 16.a Solve the following Crypt arithmetic problem using constraints satisfaction search 15
procedure. [N/D 2023]
CROSS+ROADS= DANGER
OR
CO4 An 16.b Consider five points {x1,x2,x3,x4,x5} with the following coordinates as a two- 15
dimensional sample for clustering:
X1=(0.5,1.75),x2=(1,2),x3=(1.75,0.25),x4=(4,1),x5=(6,3). Illustrate the k-means
algorithm on the above data set. The required number of clusters is two, and
initially, clusters are formed from random distribution of samples: C1= {x1,x2,x3}
and C2={x3,x5}
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