B.TechSemester 72023-24Machine LearningKOE073

Machine Learning (KOE073) - AKTU Question Paper 2023-24

B.Tech · Semester 7 · Free PDF Download

This is the official AKTU Machine Learning Previous Year Question Paper for B.Tech Semester 7, academic session 2023-24. Published by Dr. A.P.J. Abdul Kalam Technical University (AKTU/UPTU), Lucknow. Free PDF download — no login required.

Course:B.Tech

Semester:Semester 7

Session:2023-24

University:AKTU / UPTU

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Questions Asked in 2023-24

Machine Learning (KOE073) — complete question paper · 100 marks · 3 Hours

Section AAttempt all q u e s t i o n s i n b r i e f . 2 x 10 = 20

a
What is machine learning? 2
b
What are the steps involve d in designing learning system in machine learning? 2
c
Explain Artificial Neural Network. 2
d
What do you understand by gradient descent 2
e
Explain Bayes Classifier. 2
f
What are the basics o f sampling theory? 2
g
What is mistake bound model of learning? 2
h
Explain Case based Learning. 2 i. How do you evaluate the perform ance of a model based on first-order rules? 2 j. What is Reinforcement Learning? 2

Section BAttempt any three o f t h e f o l l o w i n g : 10x 3 = 30

a
Explain the Differentiate bet ween Supervised, Unsupervised and Reinforcement Learning
b
Define the following in decision tree algorithm: (i) Entropy , (ii) Information gain, (iii)Gini index, (iv) Gain Ratio, (iv)Chi-Square
c
Explain Expectation Max imization algorithm. 10
d
Explain Backpropagation algor ithm in artificial neural netwo rk (ANN) with suitable example
e
Explain the concept of hypothesis space search in the contex t of machine learning. How does the choice of hypothesis space impact the learning process? Provide examples to illustrate the significance of hypothesis space search

Section CAttempt any one p a r t o f t h e f o l l o w i n g : 10x 1 = 10

a
Explain the concept of inductive bias in machine learning, a nd provide an example to illustrate how it influences the learning process
b
Explain the Candidate Eliminat ion algorithm in machine learning and provide a step-by-step solution for a hypothetical scenario
a
What is the differe nce between forward propagation and backward propagation in neural networks explain weight calculation for forward pass network?
b
Consider a single-layer neural network with one input neuron , one hidden neuron, and one output neuron. The activation function used is the sigmoid function. The network is trained to learn the XOR function.  Input layer: One neuron  Hidden layer: One neuron with a sigmoid activation function  Output layer: One neuron with a sigmoid activation function Input Output Weights and Biases (Initial Values):  Weight between input and hidden layer: wih 0.5  Bias at the hidden layer: bh=0  Weight between hidden and output layer: who=−0.5  Bias at the output layer: bo=0 Learning Rate: 0.1 Perform one iteration of trainin g using the given data and upda te the weights and biases
a
Suppose we have a dataset of weather conditions and correspo nding play decisions (play or not play) as follows: Day Weather Temperatu Humidity Windy Play Day 1 Sunny Hot High False No Day 2 Sunny Hot High True No Day 3 Overcast Hot High False Yes Day 4 Rain Mild High False Yes Day 5 Rain Cool Normal False Yes Day 6 Rain Cool Normal True No Day 7 Overcast Cool Normal True Yes Day 8 Sunny Mild High False No (i) If the weather is sunny, then the Player should play or not? (ii) If the level of humidity is Normal & it is windy then the player should play or not?
b
Suppose there is a rare disease that affects 1 in 10,000 peo ple. A diagnostic test for this disease has a 99% accuracy rate for both true positive s (correctly identifying a person with the disease) and true negatives (correctly identifying a person without the disease). If a randomly selected person take s the test and it comes back positive, what is the probability using bayes theore m that they actually have the disease?
a
What is mistake bound model of learning? Explain the concept , algorithm evaluation, its benefit and limitation
b
Suppose you have a dataset with the following points in a 2 -dimensional space: Data points: (2, 3), (5, 4), (9, 6), (8, 1), (7, 2) and their corresponding labels are: A, A, B, B, B . Now, we want to classify a new point (6, 5) using k-nearest neighbors with k = 3
a
Consider a binary string optimization problem where the goal is to evolve a binary string of length 8 to maximize the number of ones. You decide to use a simple genetic algorithm for this task. (i) Define the representation of an individual in the population. (ii) Specify the initialization step for the genetic algorithm. (iii) Outline the different operations used in genetic algorithm
b
What are the different types of reinforcement? Explain. 10

Question text is extracted from the official AKTU question paper PDF above. Hindi translations are omitted — every question is printed in English in the original paper. Last verified: 2026-08-23.