AKTU Machine Learning Techniques (KCS055) Question Paper 2020-21

Note: Attempt all Sections. If require any missing data; then choose suitably.

Section A

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

1. Explain the concept of machine learning.
2. Compare ANN and Bayesian networks.
3. What is the difference between linear and logistic regression?
4. Discuss support vectors in SVM?
5. Discuss overfitting and underfitting situation in decision tree learning.
6. What is the task of the E-step of the EM-algorithm?
7. Define the learning classifiers.
8. What is the difference between machine learning and deep learning?
9. What objective function do regression trees minimize?
10. What is the difference between Q learning and deep Q learning?

Section B

Q2. Attempt any three of the following

1. Apply KNN for following dataset and predict class of test example
   (A1=3, A2=7).Assume K=3
A1 A2 Class
7 7 True
7 4 True
3 4 False
1 4 True
5 3 False
6 3 True
2. Describe the Kohonen Self-Organizing Maps and its algorithm.
3. Explain the various learning models for reinforcement learning.
4. Explain the role of genetic algorithm? Discuss the various phases considered in genetic algorithm.
5. Describe BPN algorithm in ANN along with a suitable example.

Section C

Q3

Attempt any one part of the following

1. Why SVM is an example of a large margin classifier? Discuss the different kernels functions used in SVM.
2. Explain the relevance of CBR. How CADET tool employs CBR?

Q4

Attempt any one part of the following

1. Discuss the applications, properties, issues, and disadvantages of SVM.
2. Explain the Confusion Matrix with respect to Machine Learning Algorithms.

Q5

Attempt any one part of the following

1. Illustrate the operation of the ID3 training example. Consider information gain as attribute measure.
2. Describe Markov Decision Process in reinforcement learning.

Q6

Attempt any one part of the following

1. What is instance based learning? How Locally Weighted Regression is different from Radial basis function networks?
2. How is Bayes theorem used in machine learning? How naive Bayes algorithm is different from Bayes theorem?

Q7

Attempt any one part of the following

1. Compare regression, classification and clustering in machine learning along with suitable real life applications?
2. Given below is an input matrix named I, kernel matrix, calculate the Convoluted matrix C using stride =1 also apply max pooling on C.

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