CS3491 Artificial Intelligence and Machine Learning Important Questions

CS3491 Artificial Intelligence and Machine Learning Important Questions

Unit 1 Part B

1. Differentiate Blind Search and Heuristic Search.
2. Explain characteristics of intelligent agents.
3. Explain iterative deepening search algorithm with an example.
4. Discuss in detail about hill climbing algorithm by using 8-queens problem.
5. Outline the uniformed search strategies like breadth-first search and depth-first search with examples.
6. State the constraint satisfaction problem. Outline local search for constraint satisfaction problem with an example.

Unit 2 Part B

1. Consider the following set of propositions:
   Patient has spots
   Patient has measles
   Patient has high fever
   Patient has Rocky mountain spotted fever.
   Patient has previously been inoculated against measles.
   Patient was recently bitten by a tick
   Patient has an allergy.
2. Create a network that defines the casual connections among these nodes.
3. Make it a Bayesian network by constructing the necessary conditional probability matrix.
4. Demonstrate the use of Bayes’ rule with an example in a doctor finding the probability P (disease / symptoms) before and after the decease becomes epidemic.
5. Briefly explain about how the sustainability of enumeration algorithm can be improved.
6. Elaborate on unconditional probability and conditional probability with an example.
7. What is a Bayesian network? Explain the steps followed to Neonloutant ausconstruct a Bayesian network with an example.
8. What do you mean by inference in Bayesian networks? Outline inference by enumeration with an example.
9. Construct a Bayesian Network and define the necessary CPTs for the given scenario. We have a bag of three biased coins a, b and c with probabilities of coming up heads of 20%, 60% and 80% respectively. One coin is drawn randomly from the bag (with equal likelihood of drawing each of the three coins) and then the coin is flipped three times to generate the outcomes X1, X2 and X3.
   (i) Draw a Bayesian network corresponding to this setup and define the relevant CPTs.
   (ii) Calculate which coin is most likely to have been drawn if the flips come up HHT.

Unit 3 Part B

1. State when and why you would use random forests vs SVM?
2. Explain the principle of the gradient descent algorithm. Accompany your explanation with a diagram.
3. Describe the general procedure of random forest algorithm.
4. With a suitable example explain knowledge extraction in detail.
5. Elaborate on logistics regression with an example. Explain the process of computing coefficients.
6. What is a classification tree? Explain the steps to construct a classification tree. List and explain about the different procedures used.

Unit 4 Part B

1. Explain various learning techniques involved in unsupervised learning.
2. List the applications of clustering and identify advantages and disadvantages of clustering algorithms.
3. Assume an image has pixel size 240 x 180. Elaborate how K means clustering can be used to achieve lossy data compression of that image.
4. Explain in detail about combining multiple classifiers by voting.
5. What is bagging and boosting? Give example.
6. Outline the steps in the AdaBoost algorithm with an example.
7. Elaborate on the steps in expectation-maximization algorithm.

Unit 5 Part B

1. Draw the architecture of a single layer perceptron (SLP) and explain its operation. Mention its advantages and disadvantages.
2. How do you tune hyperparameters for better neural network performance? Explain in detail.
3. Elaborate the process of training hidden layers by ReLU in deep networks.
4. Briefly explain hints and the different ways it can be used.
5.  Explain the steps in the back propagation learning algorithm. What is the importance of it in designing neural networks?
6. Explain a deep feedforward network with a neat sketch.