AKTU Machine Learning Techniques (BCS055) Question Paper 2025-26

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

Section A

Q1. Attempt all questions in brief · 2 × 7 = 14 marks

1. Define Machine Learning.
2. What is a well-defined learning problem?
3. State any two applications of Logistic Regression.
4. What is Entropy in Decision Tree Learning?
5. Define Support Vector Machine (SVM).
6. What is backpropagation in neural networks?
7. What is Q-learning?

Section B

Q2. Attempt any three of the following · 7 × 3 = 21 marks

1. Explain types of learning in Machine Learning with suitable examples.
2. Describe Linear Regression and its assumptions.
3. Explain Naïve Bayes classifier with an example.
4. Discuss the properties and issues of Support Vector Machines.
5. Explain k-Nearest Neighbour (k-NN) algorithm and its limitations.

Section C

Q3

Attempt any one part of the following · 7 × 1 = 7 marks

1. Explain the ID3 algorithm for Decision Tree learning.
2. Discuss inductive bias and inductive inference in decision trees.

Q4

Attempt any one part of the following · 7 × 1 = 7 marks

1. Explain perceptron and multilayer perceptron with neat diagram.
2. Derive the Backpropagation algorithm for training neural networks.

Q5

Attempt any one part of the following · 7 × 1 = 7 marks

1. Explain the architecture of Convolutional Neural Networks (CNN).
2. Discuss applications of Deep Learning with suitable case studies.

Q6

Attempt any one part of the following · 7 × 1 = 7 marks

1. Explain Reinforcement Learning with Markov Decision Process.
2. Describe Q-Learning algorithm and its applications.

Q7

Attempt any one part of the following · 7 × 1 = 7 marks

1. Explain Genetic Algorithm cycle with suitable example.
2. Analyze the role of crossover and mutation in Genetic Algorithms.

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