1. What is needed to make probabilistic systems feasible in the world?

2. The Expectation Maximization algorithm has been used to identify conserved domains in unaligned proteins only.

3. Which variable can give the concrete form to the representation of the transition model?

4. Which of the following about MEME is untrue?

5. Imagine you are dealing with text data. To represent the words you are using word embedding (Word2vec). In word embedding, you will end up with 1000 dimensions. Now, you want to reduce the dimensionality of this high dimensional data such that, similar words should have a similar meaning in nearest neighbor space.In such case, which of the following algorithm are you most likely choose?

6. If we have variables x1, x2, x3,.

7. How the entries in the full joint probability distribution can be calculated?

8. Where does the bayes rule can be used?

9. The Bayesian network graph does not contain any cyclic graph. Hence, it is known as a

10. Which reveals an improvement in online smoothing?

11. We usually use feature normalization before using the Gaussian kernel in SVM. What is true about feature normalization? We do feature normalization so that new feature will dominate other - Some times, feature normalization is not feasible in case of categorical variables Feature normalization always helps when we use Gaussian kernel in SVM"

12. Which condition is used to influence a variable directly by all the others?

13. In above question suppose you want to change one of it's(SVM) hyperparameter so that effect would be same as previous questions i.e model will not under fit?

14. In t-SNE algorithm, which of the following hyper parameters can be tuned?

15. A knowledge engineer has the job of extracting knowledge from an expert and building the expert system knowledge base.

16. Which of the following statement is correct for t-SNE and PCA?

17. What are the possible values of the variable?

18. How many terms are required for building a bayes model?

19. The nodes and links form the structure of the Bayesian network, and we call this the ?

20. The minimum time complexity for training an SVM is O(n2). According to this fact, what sizes of datasets are not best suited for SVM's?

21. Which algorithm is used for solving temporal probabilistic reasoning?

22. In EM algorithm, as an example, suppose that there are 10 DNA sequences having very little similarity with each other, each about 100 nucleotides long and thought to contain a binding site near the middle 20 residues, based on biochemical and genetic evidence. the following steps would be used by the EM algorithm to find the most probable location of the binding sites in each of the ______ sequences.

23. Forward chaining systems are _____________ where as backward chaining systems are ___________

24. Which allows for a simple and matrix implementation of all the basic algorithm?

25. In which of the following scenarios is t-SNE better to use than PCA for dimensionality reduction while working on a local machine with minimal computational power?