AKTU MCA Machine Learning (KCA054) Question Paper 2024-25

Note: Attempt all Sections. In case of any missing data; choose suitably.

Section A

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

1. Define clustering.
2. List out the four issues in Machine Learning.
3. Can Logistic Regression be used for classification? Explain it.
4. Explain the termhyperplane in SVM.
5. Illustrate the issues in decision tree learning.
6. Explain information gain.
7. Explain unsupervised learning with an example
8. Explain Self Organizing Map (SOM).
9. Describe reinforcement learning.
10. Explain Markov Decision process.

Section B

Q2. Attempt any three of the following · 10 × 3 = 30 marks

1. How is the clustering approach different from decision tree learning?Point out the reasons.
2. Explain the Naïve Bayes classifier and its principle.
3. Explain Instance-Based Learning. Compare Locally Weighted Regression and Radial Basis Function Networks.
4. Explain the concept of perceptron along with its working with a suitable example.
5. Explain in detail about Genetic Algorithm, its components and its applications

Section C

Q3

Attempt any one part of the following · 10 × 1 = 10 marks

1. Compare between Machine Learning and Data Science. Mention one challenge faced in Machine Learning and explain why it's an issue.
2. Describe the architecture of an artificial neural network. State the learning parameters used in ANN.

Q4

Attempt any one part of the following · 10 × 1 = 10 marks

1. What is the importance of support vector kernel? Explain its types.
2. Express your understanding of Concept Learning. Also describe in briefly the algorithms used for Concept Learning.

Q5

Attempt any one part of the following · 10 × 1 = 10 marks

1. Describe ID-3 algorithm with an example.
2. Explain k-Nearest Neighbor Learning algorithm with an example.

Q6

Attempt any one part of the following · 10 × 1 = 10 marks

1. Explain Types of Gradient Descent. Also explain Delta Rule and related algorithm.
2. Describe CNN architecture. Specify the function of each layer.

Q7

Attempt any one part of the following · 10 × 1 = 10 marks

1. Explain Q-learning and Deep Q-learning in detail.
2. Analyze the components of Reinforcement Learning along with its three different models.

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