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AL-702 (B) · Advance Machine Learning/Important Questions

Advance Machine Learning (AL-702 (B)) - Important Questions

  1. 7 Marks High Priority Asked: 2025

    Define Artificial Neural Networks. What is the role of the cost function in training Artificial Neural Networks?

    Appeared 1x (2025)

  2. 7 Marks High Priority Asked: 2025

    Describe the role of gradient descent in the backpropagation process and how it influences weight updates.

    Appeared 1x (2025)

  3. 7 Marks High Priority Asked: 2025

    Describe the architecture and learning process of a multi-layer perceptron.

    Appeared 1x (2025)

  4. 7 Marks High Priority Asked: 2025

    What is recursive induction in decision trees? Explain.

    Appeared 1x (2025)

  5. 7 Marks High Priority Asked: 2025

    Define entropy and information gain. Calculate the overall entropy and information gain with the following dataset.

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  6. 7 Marks High Priority Asked: 2025

    How does noisy data affect decision tree performance? Write the strategies to overcome them.

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  7. 7 Marks High Priority Asked: 2025

    Compare Bagging and Boosting techniques in ensemble learning.

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  8. 7 Marks High Priority Asked: 2025

    Describe the working mechanism of a Random Forest algorithm.

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  9. 7 Marks High Priority Asked: 2025

    Why ensemble methods offer greater robustness compared to individual models? Discuss.

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  10. 14 Marks High Priority Asked: 2025

    Write short note on any two :

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  11. 7 Marks High Priority Asked: 2025

    Explain the Bellman Equations in the context of Reinforcement Learning.

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  12. 7 Marks High Priority Asked: 2025

    Differentiate between Q-learning and SARSA algorithms.

    Appeared 1x (2025)

  13. 7 Marks High Priority Asked: 2025

    Describe the concept of Policy Gradient methods in Reinforcement Learning.

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  14. 7 Marks High Priority Asked: 2025

    What is temporal difference learning and how does it differ from Monte Carlo methods?

    Appeared 1x (2025)

  15. 7 Marks High Priority Asked: 2025

    How does Generative Adversarial Imitation learning differ from standard RL approaches?

    Appeared 1x (2025)

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