AKTU Machine Learning Techniques (BCDS062) 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 and list its different types.
2. Explain the concept of well-defined learning problem with suitable example.
3. State Bayes Theorem.
4. Explain the concept of overfitting in Decision Trees?
5. Define k-Nearest Neighbor algorithm.
6. Define activation function in neural networks.
7. Define Reinforcement Learning with one example.

Section B

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

1. Explain the design of a learning system with a suitable example.
2. Describe Linear Regression and Logistic Regression with differences.
3. Explain the working of Naïve Bayes Classifier with assumptions.
4. Discuss Information Gain and its role in Decision Tree Learning.
5. Explain types of kernels used in Support Vector Machines.

Section C

Q3

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

1. Explain various Machine Learning approaches such as ANN, Clustering, and Reinforcement Learning.
2. Discuss issues in Machine Learning and differentiate between Data Science and Machine Learning.

Q4

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

1. Explain Bayes Optimal Classifier and Bayesian Belief Networks.
2. Describe the EM Algorithm with suitable example.

Q5

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

1. Explain ID3 Algorithm with an example.
2. Discuss k-Nearest Neighbor and Locally Weighted Regression.

Q6

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

1. Derive the Backpropagation Algorithm for Multilayer Neural Networks.
2. Explain CNN architecture and different types of layers.

Q7

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

1. Explain Q-Learning algorithm and Markov Decision Process.
2. Describe Genetic Algorithm cycle including crossover and mutation.

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