Machine Learning - R_23 - JNTUK, Model Paper - 1

JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY KAKINADA

B.Tech III Year II Semester – R23

MACHINE LEARNING

Time: 3 Hours Max Marks: 70

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SECTION – I (50 Marks)

Answer ALL questions
(Each question carries 10 marks: a) 5 + b) 5)

UNIT – I (Introduction to Machine Learning)

1.

a) Define Machine Learning. Explain its evolution with examples. (5M)
b) Describe stages in Machine Learning process with neat diagram. (5M)

2.

a) Differentiate Rote Learning, Induction, and Reinforcement Learning. (5M)
b) Explain Data Acquisition and Feature Engineering. (5M)

UNIT – II (Nearest Neighbor-Based Models)

3.

a) Explain distance measures in KNN with formulas. (5M)
b) Classify test point (3,4) using KNN (k=3):
(1,2)-A, (2,3)-A, (5,6)-B, (6,7)-B (5M)

4.

a) Explain non-metric similarity functions & binary proximity. (5M)
b) Discuss KNN performance issues & improvements. (5M)

UNIT – III (Decision Trees & Bayesian Models)

5.

a) Explain Decision Trees with Entropy & Gini Index. (5M)
b) Construct decision tree using Information Gain. (5M)

6.

a) Explain Bias–Variance trade-off. (5M)
b) Describe Naive Bayes classifier & applications. (5M)

UNIT – IV (Linear Discriminants & Neural Models)

7.

a) Explain Linear Discriminant Functions. (5M)
b) Describe Perceptron Learning Algorithm with flow diagram. (5M)

8.

a) Explain SVM and kernel trick. (5M)
b) Explain MLP architecture and Backpropagation. (5M)

UNIT – V (Clustering)

9.

a) Define clustering. Explain Partitional & Hierarchical methods. (5M)
b) Explain K-Means algorithm with steps & advantages. (5M)

10.

a) Explain Fuzzy C-Means and compare with K-Means. (5M)
b) Describe EM-based clustering & applications. (5M)

SECTION – II (20 Marks)

(Each question carries 2 marks)

1. Define supervised and unsupervised learning.
2. What is feature selection?
3. Define Euclidean distance.
4. What is radius-based nearest neighbor algorithm?
5. Define entropy in decision trees.
6. What is random forest?
7. Define linear separability.
8. What is a kernel function?
9. Define soft clustering.
10. What is spectral clustering?