Machine Learning Techniques (BCS055) AKTU previous year questions 2021–2026
Every Machine Learning Techniques question from 9 AKTU papers, tagged by unit, topic and marks. A few recent questions from each unit are listed below; open the page to filter by unit, topic or mark type.
Unit 1: Introduction – AKTU PYQs
- Define Machine Learning and list its different types. (2 marks, 2026, Types of Learning)
- Define Machine Learning. (2 marks, 2026, Learning)
- Explain the concept of well-defined learning problem with suitable example. (2 marks, 2026, Well-Defined Learning Problems)
- What is a well-defined learning problem? (2 marks, 2026, Well-Defined Learning Problems)
- Explain the design of a learning system with a suitable example. (7 marks, 2026, Designing a Learning System)
Unit 2: Regression, Bayesian Learning and Support Vector Machine – AKTU PYQs
- State Bayes Theorem. (2 marks, 2026, Bayes Theorem)
- State any two applications of Logistic Regression. (2 marks, 2026, Logistic Regression)
- Define Support Vector Machine (SVM). (2 marks, 2026, Introduction to Support Vector Machine)
- Describe Linear Regression and Logistic Regression with differences. (7 marks, 2026, Logistic Regression)
- Describe Linear Regression and its assumptions. (7 marks, 2026, Linear Regression)
Unit 3: Decision Tree Learning and Instance-Based Learning – AKTU PYQs
- Explain the concept of overfitting in Decision Trees? (2 marks, 2026, Issues in Decision Tree Learning)
- What is Entropy in Decision Tree Learning? (2 marks, 2026, Entropy and Information Theory)
- Define k-Nearest Neighbor algorithm. (2 marks, 2026, k-Nearest Neighbour Learning)
- Discuss Information Gain and its role in Decision Tree Learning. (7 marks, 2026, Information Gain)
- Explain k-Nearest Neighbour (k-NN) algorithm and its limitations. (7 marks, 2026, k-Nearest Neighbour Learning)
Unit 4: Artificial Neural Networks and Deep Learning – AKTU PYQs
- Define activation function in neural networks. (2 marks, 2026, Types of CNN Layers)
- What is backpropagation in neural networks? (2 marks, 2026, Derivation of Backpropagation Algorithm)
- Explain perceptron and multilayer perceptron with neat diagram. (7 marks, 2026, Multilayer Perceptron)
- Derive the Backpropagation algorithm for training neural networks. (7 marks, 2026, Derivation of Backpropagation Algorithm)
- Explain the architecture of Convolutional Neural Networks (CNN). (7 marks, 2026, Concept of Convolutional Neural Network)
Unit 5: Reinforcement Learning and Genetic Algorithms – AKTU PYQs
- Define Reinforcement Learning with one example. (2 marks, 2026, Introduction to Reinforcement Learning)
- What is Q-learning? (2 marks, 2026, Q Learning)
- Explain Reinforcement Learning with Markov Decision Process. (7 marks, 2026, Markov Decision Process)
- Describe Q-Learning algorithm and its applications. (7 marks, 2026, Q Learning)
- Explain Q-Learning algorithm and Markov Decision Process. (7 marks, 2026, Q Learning)