AKTU Machine Learning Techniques (BCS055) Question Paper 2024-25

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. What constitutes a well-defined learning problem in machine learning? Give an example.
2. How does machine learning differ from data science, and how do they complement each other?
3. Explain the role of the sigmoid function in logistic regression.
4. What is the significance of the decision surface in Support Vector Machines (SVM)?
5. What is overfitting in decision tree learning, and how can it be avoided?
6. Explain the role of the activation function in a perceptron.
7. Describe the role of the reward function in reinforcement learning.

Section B

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

1. Describe the steps involved in designing a machine learning system. Illustrate each step with a real-world example.
2. Explore the difference between Simple Linear Regression and Multiple Linear Regression. How does the inclusion of multiple features affect the model?
3. Illustrate the steps of the ID3 algorithm, including how it selects attributes and constructs the tree.
4. Discuss the Self-Organizing Map (SOM) algorithm. How does SOM work for clustering and dimensionality reduction? Explain the differences between SOM and supervised learning models.
5. What is the role of the Q-learning function in reinforcement learning? Explain how it helps the agent learn optimal actions and policies.

Section C

Q3

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

1. Discuss the history of machine learning. Highlight major milestones and advancements in the field.
2. Compare and contrast supervised, unsupervised, and reinforcement learning. Provide examples where each type is applicable.

Q4

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

1. Consider the following weather data to determine whether or not a person will play tennis on a given day based on weather conditions:
   Day Outlook Temperature Humidity Wind Play Tennis?
   1 Sunny Hot High Weak No
   2 Overcast Hot High Weak Yes
   3 Rainy Cool Normal Weak No
   4 Overcast Cool Normal Strong Yes
   5 Rainy Mild Normal Weak Yes
   6 Sunny Mild Normal Strong No
   7 Overcast Mild High Strong Yes
   8 Overcast Hot Normal Weak No
   Apply Naïve Bayes Classifier to predict whether the person will play or not play tennis on given condition {Outlook = Sunny, Temperature =
   Cool, Humidity = High, Wind = Strong}
2. Fit a linear regression model for the dataset ( 𝑥 , 𝑦ሻ : (1, 1.5), (2, 3.0), (3, 4.5), (4, 6.0) and predict 𝑦 for 𝑥 = 5.

Q5

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

1. Describe the concept of locally weighted regression (LWR). How does it differ from traditional regression models, and what are its practical applications?
2. Given the following dataset with two attributes (A and B), and a target variable (T):
   A 1 2 1 2 1 2
B 2 3 3 2 2 3
T Yes No Yes No Yes No
   Calculate the entropy before the split and after the split on attribute A.
   Which attribute (A or B) gives the highest information gain?

Q6

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

1. Derive the mathematical steps involved in backpropagation for training a neural network.
2. Explain the architecture and functioning of Convolutional Neural Networks (CNNs).

Q7

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

1. Explain the concept of Reinforcement Learning (RL). Discuss how it differs from supervised and unsupervised learning.
2. Explain the components of a genetic algorithm. Discuss the role of chromosomes, genes, and the population in the GA cycle.

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