JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD B.Tech IV MACHINE LEARNING Previous Question Paper (August/September - 2023 - R16)
Note: i) Question paper consists of Part A, Part B. ii) Part A is compulsory, which carries 25 marks. In Part A, Answer all questions. iii) In Part B, Answer any one question from each unit. Each question carries 10 marks and may have a, b as sub questions.
Part A
What are the issues in decision tree learning?
Answer
The primary issues in decision tree learning are overfitting, where the model captures noise instead of the underlying pattern, and the greedy search approach, which may lead to locally optimal rather than globally optimal tree structures.
List out the issues in machine learning.
Answer
Key issues in machine learning include:
- Overfitting and Underfitting: Balancing model complexity with generalization.
- Data Quality: Handling noisy, incomplete, or biased datasets.
- Computational Complexity: Managing the trade-off between training time and model accuracy.
Give the representation of artificial neural networks.
Answer
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Give example to explain the estimation of hypothesis accuracy.
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What is Bayesian belief network?
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Identify the disadvantage of K- NN algorithm?
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What is the motivation of genetic algorithms?
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What are rewards and actions in reinforcement learning?
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Define analytical learning.
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Discuss about the augment search operators.
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Part B
Explain find –S algorithm with given example. Give its application to the following data.
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What is the procedure of building Decision tree using ID3 with Gain and Entropy? Illustrate with example.
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Derive the Backpropagation rule considering the training rule for Output Unit weights and Training Rule for Hidden Unit weights.
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Explain in detail about the general approach for deriving the confidence intervals.
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Explain in detail about the maximum likelihood hypotheses for predicting probabilities.
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Explain minimum description length principle.
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Consider the training dataset given in the following table. Use weighted k-NN and determine the class. Test instance (7.6, 60, 8) and K = 3.
| Sno | CGPA | Assessment | Project submitted | Result |
|---|---|---|---|---|
| 1 | 9.2 | 85 | 8 | Pass |
| 2 | 8 | 80 | 7 | Pass |
| 3 | 8.5 | 81 | 8 | Pass |
| 4 | 6 | 45 | 5 | Fail |
| 5 | 6.5 | 50 | 4 | Fail |
| 6 | 8.2 | 72 | 7 | Pass |
| 7 | 5.8 | 38 | 5 | Fail |
| 8 | 8.9 | 91 | 9 | Pass |
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Write the steps and explain the concept of sequential covering algorithms.
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Describe in detail about the parallelizing genetic algorithms? Give examples.
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Illustrate the concept of explanation-based learning of search control knowledge with an example.
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Describe how prior knowledge is used to initialize the hypothesis?
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