AKTU Machine Learning (KME074) Question Paper 2023-24

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

Section A

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

1. Define Machine Learning and briefly explain its significance in today’s technological landscape.
2. Differentiate between Artificial Intelligence (AI) and Machine Learning (ML), highlighting their key distinctions.
3. What is the main difference between classification and regression analysis in supervised learning?
4. Provide a brief overview of the types of support vector kernels.
5. Explain the Multidimensional Scaling.
6. How does K-Means Clustering work in unsupervised learning?
7. Define the Back propagation Algorithm in neural networks.
8. Outline the basics of the Decision Tree algorithm.
9. Explain the meaning of reproduction in genetic algorithm
10. Explain the difference between reinforcement learning and deep learning.

Section B

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

1. Explain the fundamental concepts of machine learning and discuss its significance in various fields. Provide examples of real-world applications of machine learning in mechanical engineering.
2. Discuss the concept of bias and variance in the context of evaluating an estimator. How do these factors impact the performance of a machine learning model, and what strategies can be employed to strike a balance between them?
3. Explain the principles of unsupervised learning and delve into the workings of K-Means Clustering and the Expectation-Maximization Algorithm. Provide real-world examples where these techniques can be effectively applied.
4. Explore the basics of Decision Trees, focusing on the ID3 Algorithm and the role of information gain and entropy. Discuss the challenges associated with decision tree learning and potential solutions.
5. Discuss the Genetic algorithm (GA) with suitable example. Also explain its advantages and applications.

Section C

Q3

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

1. Discuss the key components involved in designing a machine learning system. Provide insights into the challenges and considerations during the design phase.
2. Differentiate between data science and machine learning. Discuss the overlap and unique aspects of these two fields.

Q4

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

1. Explore the principles of Support Vector Machines (SVM). Discuss the types of support vector kernels and the challenges associated with SVM. Provide a case study on car price prediction using SVM.
2. What is regression in machine learning? Explain with examples.

Q5

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

1. Draw the cluster of following 8 points into 3 clusters:
   A1= (10,7), A2=(8,6), A3=(9,4), A4=(5,8), A5=(7,5), A6=(7,4), A7=(3,2), A8=(4,9). Use the
   k-means algorithm and Euclidean distance and take the Initial cluster centers are A2(8, 6),
   A4(5, 8) & A8 (4,9). The solution up to two iterations.
2. Explore Multidimensional Scaling and Linear Discriminant Analysis in the context of unsupervised learning. How do these methods contribute to data analysis and pattern recognition?

Q6

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

1. Introduce neural networks, covering the perceptron and the Back propagation Algorithm. Explain the convergence analysis and the universal approximation theorem for the back propagation algorithm.
2. Explore the concept of Convolutional Neural Networks (CNNs) and the different types of layers in CNN architecture. Provide a case study demonstrating the application of CNN in real-world scenarios such as self-driving cars or building a smart speaker.

Q7

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

1. What is genetic algorithm? Explain with suitable example and give its advantages.
2. Examine the principles of Reinforcement Learning and its role in training intelligent systems. Compare and contrast reinforcement learning with supervised and unsupervised learning. Provide examples of real-world scenarios where reinforcement learning has been successfully applied.

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