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Advanced Machine Learning Questions
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Advanced Machine Learning Questions
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25 Questions

1. State the primal objective for a support vector machine

2. Soft Margin Support Vector Machine Objective Optimisation

3. K Means Clustering Advantages and Disadvantages

4. Define Gibbs Sampling

5. What is the multivariate MLE value for Naive Bayes?

6. What are maximum margin classifiers?

7. Perceptron Objective Function

8. Soft Margin Support Vector Machine Advantages and Disadvantages

9. K Means Clustering Objective Optimisation

10. What is regularisation and generalisation?

11. What does EM aim to do?

12. Define a Hidden Markov Model

13. K Nearest Neighbour Objective Optimisation

14. Model Compression

15. What is the margin?

16. Empirical risk minimization

17. State the Classification Function of a SVM

18. Perceptron Advantages and Disadvantages

19. Define Accuracy:

20. Define Recall/Sensitivity

21. basin hopping

22. K Nearest Neighbour Advantages and Disadvantages

23. State the canonical representation of the hyperplane with maximum margin classifiers, and define support vectors

24. What is the formula for a Maximum Likelihood Estimate?

25. What is PCA?

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