AKTU Machine Learning Techniques (KAI601) Question Paper 2023-24

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

Section A

Q1. Attempt all questions in brief

1. Name two types of learning commonly used in machine learning.
2. Give an example of a supervised learning problem.
3. What is logistic regression, and how does it differ from linear regression?
4. What are the three types of support vector kernels commonly used in SVMs?
5. Define inductive bias in the context of decision tree learning.
6. Describe the process of locally weighted regression in instance-based learning.
7. Define perceptron and their role in artificial neural networks.
8. What are the key characteristics of the Self-Organizing Map (SOM) algorithm?
9. Define Reinforcement Learning (RL) and explain its key components.
10. Discuss the components of a genetic algorithm.

Section B

Q2. Attempt any three of the following

1. Compare and contrast supervised, unsupervised, and reinforcement learning approaches 10 in machine learning.
2. Discuss the mathematical formulation of linear regression, including the hypothesis 10 function, cost function, and optimization algorithm used for parameter estimation.
3. Provide a detailed explanation of the ID3 algorithm used for constructing decision trees. Discuss the key steps involved in the iterative process of feature selection and node splitting .
4. Explain the fundamental concepts behind the Backpropagation Algorithm and its 10 importance in training neural networks.
5. Explain the Q-learning algorithm and its role in Reinforcement Learning.

Section C

Q3

Attempt any one part of the following

1. Discuss the role of Bayesian networks in representing probabilistic relationships 10 between variables. Explain how Bayesian networks are constructed, updated, and utilized for inference in real-world scenarios.
2. Discuss the concept of model evaluation in machine learning. Compare and contrast 10 evaluation metrics such as accuracy, precision, recall and F1-score.

Q4

Attempt any one part of the following

1. Discuss the fundamental principles of the Expectation-Maximization (EM) algorithm 10 and its role in probabilistic modeling and parameter estimation.
2. Define the concept of a hyperplane in the context of machine learning and Support 10 Vector Machines (SVMs). Explain how hyperplanes are used as decision surfaces to separate classes in feature space.

Q5

Attempt any one part of the following

1. Discuss how entropy measures the uncertainty or randomness of a dataset and its role in 10 quantifying the impurity of decision tree nodes.
2. Describe the Locally Weighted Regression (LWR) technique and its purpose in machine 10 learning.

Q6

Attempt any one part of the following

1. Examine the effect of pooling layer parameters, such as pooling size and stride, on 10 feature representation and network performance.
2. Describe the role of Convolutional Neural Networks (CNNs), in diagnosing Diabetic 10 Retinopathy.

Q7

Attempt any one part of the following

1. Critically analyze the strengths and limitations of Genetic Programming compared to 10 other machine learning techniques.
2. Critically analyze the trade-offs between exploration and exploitation in the GA cycle of 10 reproduction.

Short on time? See the most repeated AKTU Machine Learning Techniques questions across all years.

Other years

More AKTU Machine Learning papers on LearnAIFast.

This paper is reproduced for study purposes only. Copyright belongs to AKTU. Source: the question paper bank of ABES Institute of Technology, an AKTU-affiliated college. If you own the copyright and want it removed, email learnwithskillnest@gmail.com and we will take it down.