Question Paper Code : 40924
B.E./B.Tech. DEGREE EXAMINATIONS, NOVEMBER/DECEMBER 2024.
Fourth/Sixth Semester
Computer Science and Engineering
CS 3491 — ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
(Common to : Biomedical Engineering/Computer Science and Design/Computer Science and Engineering (Cyber Security)/Computer and Communication Engineering/Electronics and Communication Engineering/Electronics and Telecommunication Engineering/Medical Electronics/Information Technology)
(Regulations 2021)
Answer ALL questions.
PART A — (10 × 2 = 20 marks)
- 1.
Define artificial intelligence in terms of human performance.
- 2.
Define uniformed search.
- 3.
What is Bayesian inference?
- 4.
How are Bayesian networks different from casual inference?
- 5.
Compare linear and logistic regression.
- 6.
List the advantages of SVM.
- 7.
Mention the uses of K-means algorithm.
- 8.
What is bagging and boosting in machine learning?
- 9.
How does the perceptron make its information into action?
- 10.
Mention the use of ReLU.
PART B — (5 × 13 = 65 marks)
- 11.(a)
Explain Constraint Satisfaction Problems in Artificial Intelligence.
- Or
- (b)
Explain heuristic search strategy with example.
- 12.(a)
Explain Bayes' Theorem and discuss the working of Naive Bayes' Classifier.
- Or
- (b)
Elaborate Bayesian networks with example.
- 13.(a)
Why do we use Regression Analysis? Explain the different types of Regression.
- Or
- (b)
Illustrate how Logistic regression used to solve the classification problems.
- 14.(a)
Explain the three types of ensemble learning in detail.
- Or
- (b)
How does KNN work? Illustrate with example.
- 15.(a)
Discuss multi layer perceptron in detail.
- Or
- (b)
How error function is used in Backpropagation and how does Backpropagation work?
PART C — (1 × 15 = 15 marks)
- 16.(a)
Bayesian statistics lie at the heart of most statistical reasoning systems. How is Bayes theorem exploited? Illustrate with example.
- Or
- (b)
Which is more stable decision tree or random forest? Justify your answer with example.