AKTU MCA Machine Learning (BMC054) Question Paper 2025-26

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

Section A

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

1. Define machine learning. Mention any two real - life applications of machine learning.
2. What is a well - defined learning problem? State its three components.
3. Define overfitting in machine learning.
4. State Bayes theorem and mention its use in classification.
5. What is information gain in decision tree learning?
6. Define deep learning and mention one of the latest trends in machine learning.
7. What is meant by model optimization? Mention one measure used to report expected accuracy.

Section B

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

1. Explain the types of learning in machine learning. Differentiate supervised, unsupervised and reinforcement learning with suitable examples.
2. Apply linear regression for prediction and explain the role of the cost function and gradient descent in model learning.
3. Discuss the latest trends in machine learning, such as deep learning, reinforcement learning and genetic algorithms, with suitable applications.
4. Design a k-nearest neighbour-based classifier for a real-world classification problem. Discuss the role of k and distance measures in prediction.
5. Explain model evaluation using accuracy, precision, recall and F1-score. Why is accuracy alone not sufficient for imbalanced data?

Section C

Q3

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

1. Explain the need for machine learning for problem-solving. Also, describe the general steps involved in designing a learning system.
2. Discuss the history and major approaches of machine learning, including artificial neural networks, clustering, decision tree learning, Bayesian networks, SVM and genetic algorithm.

Q4

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

1. Explain logistic regression. How is it different from linear regression? Describe its use in binary classification.
2. Explain Support Vector Machine. Discuss hyperplane, decision surface, support vectors and different kernel functions used in SVM.

Q5

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

1. Discuss recent trends in machine learning. Explain how deep learning and convolutional neural networks are used in real-world applications.
2. Explain reinforcement learning and deep Q-learning as the latest trends in machine learning. Discuss their practical applications.

Q6

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

1. Design an artificial neural network model for a classification problem. Discuss the role of perceptron, multilayer perceptron, activation function, weights and bias.
2. Design a convolutional neural network for an image classification problem. Explain the convolutional layer, activation function, pooling layer and fully connected layer with a suitable case study.

Q7

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

1. Explain model optimization in machine learning. Discuss how accuracy, precision, recall and F1-score can be used to report the expected performance of a learned model.
2. Explain the Markov Decision Process and the Q-learning algorithm. How can Q-values be optimized to improve the learned policy and expected accuracy?

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