Machine Learning - R_23 - JNTUK, Model Paper - 3

JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY KAKINADA

B.Tech III Year II Semester – R23

Course: 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) Explain the Evolution of Machine Learning. Highlight major milestones and applications. (5M)
b) Explain the Machine Learning system architecture and clearly describe the role of each stage. (5M)

2.

a) Explain Data Sets in Machine Learning. Discuss training, validation, and test datasets. (5M)
b) Explain Search and Learning in Machine Learning with a suitable example. (5M)

UNIT – II : Nearest Neighbor–Based Models

3.

a) Explain Distance Measures used in ML. Compare Euclidean, Manhattan, and Minkowski distances. (5M)
b) Apply Radius Nearest Neighbor algorithm to classify a test instance. (5M)

4.

a) Explain Proximity between binary patterns with suitable measures. (5M)
b) Discuss performance of nearest neighbor classifiers and methods to improve accuracy. (5M)

UNIT – III : Decision Trees & Bayesian Models

5.

a) Explain construction of decision trees with neat diagram. (5M)
b) Explain Random Forest algorithm and why it outperforms single decision tree. (5M)

6.

a) Explain Bayes Classifier Optimality. (5M)
b) Apply Naive Bayes for simple text classification. (5M)

UNIT – IV : Linear Discriminants & Neural Models

7.

a) Explain Linear Discriminants with mathematical representation. (5M)
b) Explain Non-linear SVM and kernel functions. (5M)

8.

a) Explain architecture of Multi-Layer Perceptron (MLP). (5M)
b) Explain Backpropagation with error minimization. (5M)

UNIT – V : Clustering

9.

a) Explain clustering of patterns and applications. (5M)
b) Explain Divisive clustering and compare with Agglomerative clustering. (5M)

10.

a) Explain Soft Partitioning and Soft Clustering. (5M)
b) Explain Rough K-Means clustering and its advantages. (5M)

SECTION – II (20 Marks)

(Each question carries 2 marks)

1. What is learning by induction?
2. Define model evaluation.
3. What is Minkowski distance?
4. Define similarity measure.
5. What is pruning in decision trees?
6. Define class conditional independence.
7. What is margin in SVM?
8. Define sigmoid function.
9. What is soft partitioning?
10. Define rough clustering.