AKTU Machine Learning (KOE073) Question Paper 2024-25

Note: Attempt all Sections. In case of any missing data; choose suitably.

Section A

Q1. Attempt all questions in brief · 2 × 10 = 20 marks

1. Describe the differences between well-defined and ill-defined learning problems.
2. Describe the trade-off between bias and variance in machine learning.
3. What is inductive bias in the context of decision tree learning?
4. Explain how overfitting can affect decision tree performance.
5. What is hypothesis accuracy in machine learning?
6. What is Bayes theorem?
7. Explain the relationship between sample size and generalization error for finite hypothesis spaces.
8. What is the mistake bound model of learning?
9. What is a hypothesis space in machine learning?
10. Explain the process of general-to-specific beam search in hypothesis generation.

Section B

Q2. Attempt any three of the following · 10 × 3 = 20 marks

1. Demonstrate an example of concept learning for a binary classification problem.
2. Demonstrate how Adaline adjusts its weights during training.
3. Describe the relationship between Bayes Optimal Classifier and posterior probabilities.
4. Demonstrate the implementation of k-NN for a simple classification problem.
5. Describe the advantages of FOIL over propositional rule learners.

Section C

Q3

Attempt any one part of the following · 10 × 1 = 10 marks

1. Explain the working steps of the Find-S algorithm.
2. Explain how the List-Then-Eliminate algorithm finds consistent hypotheses.

Q4

Attempt any one part of the following · 10 × 1 = 10 marks

1. Demonstrate the training of a perceptron using the perceptron learning rule.
2. Demonstrate how to update weights using the Delta rule.

Q5

Attempt any one part of the following · 10 × 1 = 10 marks

1. Explain the steps of the EM algorithm: Expectation step and Maximization step.
2. Describe how conditional probabilities are represented in Bayesian Belief Networks

Q6

Attempt any one part of the following · 10 × 1 = 10 marks

1. Demonstrate the application of the mistake bound model to the Perceptron learning algorithm.
2. Demonstrate the application of LWR to a simple regression dataset.

Q7

Attempt any one part of the following · 10 × 1 = 10 marks

1. Explain the key components of a reinforcement learning framework: states, actions, rewards.
2. Describe the significance of the learning rate and discount factor in Q-learning.

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