MACHINE LEARNING | UTU QUESTION PAPER

MACHINE LEARNING UTU QUESTION PAPER (2021-22)

MACHINE LEARNING UTU QUESTION PAPER

Roll No.

Even Semester Examination, 2021-22

Course Name: B.TECH

Branch: Computer Science & Engineering

Semester: VI

Subject: Machine Learning

Time: 3 Hours

Max Marks: 100

Number of Printed pages: 2

Note:- Attempt all questions: All Questions carry equal marks

Q1. Attempt any four parts of the following:(5 x 4 = 20)

(a) List and explain the perspectives and issues in Machine learning

(b) Define decision tree learning. Explain different problems for decision tree learning.

(c) Define Artificial Neural Network. Explain biological learning system.

(d) Discuss with examples some useful applications of machine learning.

(e) Define (i) Prior Probability (ii) Conditional Probability (iii) Posterior Probability.

Q2. Attempt any four parts of the following: (5 x 4 = 20)

(a) Discuss Concept learning as search with respect to General to specific ordering of hypothesis.

(b) Define Bayesian theorem? What is the relevance and features of Bayesian theorem?

(c) Explain gradient descent algorithm.

(d) Describe Find S Algorithm. What are the properties and complaints of Find S?

(e) What are all the Boolean functions represented by perceptron?

Q3. Attempt any two parts of the following: (10 x 2 = 20)

(a) Explain Naïve Bayes Classifier with an example.

(b) What is Reinforcement Learning and explain Reinforcement learning problem with neat diagram.

(c) What is linearly in separable problem? Design a two-layer network of perceptron to implement 

a) X OR Y   b) X AND Y

Q4. Attempt any two parts of the following: (10 x 2 = 20)

(a) Describe the derivation of back propogation rule.

(b) Explain maximum likelihood and least-squared error hypothesis.

(c) Explain the following:

i)Linear regression

ii)Logistic regression

Q5. Attempt any two parts of the following: (10 x 2 = 20)

(a) Draw and explain the architecture of Convolutional Neural Network.

(b) What is deep learning? Explain its uses and application.

(c) Discuss Semi supervised learning with its practical applications.