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AKTU Machine Learning (Open Elective) Important Questions

The questions AKTU asks again and again in Machine Learning (Open Elective), found by comparing every question in 6 previous year papers from 2022-23 to 2024-25 (subject codes KME074, KOE073).

How this list is made: we compared all 150 questions in these papers and grouped the ones that ask about the same thing. Each item shows how many papers it appeared in, which years, and other ways it was worded. Topics that come back often are a strong hint, not a guarantee, so study your full syllabus too.

Most repeated long questions

From Sections B and C (7 or 10 marks, depending on the year). Unit numbers come from Section C, where each question covers one unit.

  1. Draw the cluster of following 8 points into 3 clusters: A1=(5,10), A2=(4,6), 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(4, 6), A4(5, 8) & A8 (4,9). The solution up to two iterations.
    Asked in 3 papersUnit 3 · 2023-24 KME074 · 2022-23 KOE073 · 2022-23 KME074
    Also asked as:
    • Use the Nearest Neighbor clustering algorithm and Euclidean distance to cluster: A1=(5,4), A2=(2,5), A3=(8,4), A4=(5,8), A5=(7,5), A6=(6,4), A7=(1,2), A8=(4,9). Suppose that the threshold t is 4.
    • 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. Explain Expectation Maximization algorithm.
    Asked in 3 papersUnit 3 · 2024-25 KOE073 · 2024-25 KME074 · 2023-24 KOE073
    Also asked as:
    • What is the expectation-maximization algorithm? Provide a brief overview.
    • Explain the steps of the EM algorithm: Expectation step and Maximization step.
  3. Explain the main steps of genetic algorithm with suitable examples?
    Asked in 3 papersUnit 5 · 2023-24 KME074 · 2022-23 KOE073 · 2022-23 KME074
    Also asked as:
    • What is a genetic algorithm and its advantages? Explain with suitable example
    • What is genetic algorithm? Explain with suitable example and give its advantages.
    • Discuss the Genetic algorithm (GA) with suitable example. Also explain its advantages and applications
  4. What are the different types of reinforcement? Explain.
    Asked in 3 papersUnit 5 · 2023-24 KOE073 · 2022-23 KOE073 · 2022-23 KME074
    Also asked as:
    • What are the 4 types of reinforcement? Explain any two.
    • What do you mean by Reinforcement learning and its types? How does Reinforcement Learning Work?
  5. Construct the decision tree for the given data set & Calculate the entropy and information gain. Play DAY Outlook Temperature Humidity Windy Golf D1 Rainy Hot High Weak No D2 Rainy Hot High strong No D3 Overcast Hot High Weak Yes D4 Sunny Mild High Weak Yes D5 Sunny Cool Normal Weak Yes
    Asked in 2 papersUnit 2 · 2022-23 KOE073 · 2022-23 KME074
    Also asked as:
    • Explain the steps of decision tree making, with following data set, also calculate the Calculate data set entropy and information gain. Play DAY Outlook Temperature Humidity Sun light cricket D1 Rainy Hot High Weak No D2 Rainy Hot High strong No D3 Overcast Hot High Weak Yes D4 Sunny Mild High Weak Yes D5 Sunny Cool Normal Weak Yes
  6. Define the following in decision tree algorithm: (i) Entropy, (ii) Information gain, (iii) Gini index, (iv) Gain Ratio, (iv) Chi-Square
    Asked in 2 papers2023-24 KOE073 · 2022-23 KOE073
  7. Explain Backpropagation algorithm in artificial neural network (ANN) with suitable example.
    Asked in 2 papers2023-24 KOE073 · 2022-23 KME074
  8. Explain the Differentiate between Supervised, Unsupervised and Reinforcement Learning
    Asked in 2 papers2023-24 KOE073 · 2022-23 KME074
  9. Show the application of Clustering in various sectors, discus with following examples: Marketing, Insurance, & Earth-quake studies.
    Asked in 2 papersUnit 4 · 2022-23 KOE073 · 2022-23 KME074
    Also asked as:
    • Show the application of Clustering in various sectors, discuss with following examples: Marketing, Insurance, & Earth-quake studies.
  10. Suppose we have a dataset of weather conditions and corresponding play 10 decisions (play or not play) as follows: Day Weather Temperatu Humidity Windy Play re 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?
    Asked in 2 papersUnit 3 · 2023-24 KOE073 · 2022-23 KOE073
    Also asked as:
    • Show the following data set through Naive Bayes Classifiers: (i) If the weather is sunny, then the Player should play or not? (ii) If the level of humidity is medium & spend of the wind is high and then then the plays should play or not? Case Outlook Temperature Humidity Windy Play Golf 0 Rainy Hot High FALSE No 1 Rainy Hot High TRUE No 2 Overcast Hot High FALSE Yes 3 Sunny Mild High FALSE Yes 4 Sunny Cool Normal FALSE Yes 5 Sunny Cool Normal TRUE No 6 Overcast Cool Normal TRUE Yes 7 Rainy Mild High FALSE No 8 Rainy Cool Normal FALSE Yes 9 Sunny Mild Normal FALSE Yes 10 Rainy Mild Normal TRUE Yes 11 Overcast Mild High TRUE Yes 12 Overcast Hot Normal FALSE Yes 13 Sunny Mild High TRUE No
  11. What is regression in machine learning with example?
    Asked in 2 papersUnit 2 · 2023-24 KME074 · 2022-23 KME074
    Also asked as:
    • What is regression in machine learning? Explain with examples.
  12. What is the difference between forward propagation and backward propagation in neural networks explain weight calculation for forward pass network?
    Asked in 2 papersUnit 2 · 2023-24 KOE073 · 2022-23 KOE073

Most repeated short questions (2 marks)

From Section A. Learn a crisp definition plus one example or line of explanation for each.

  1. Describe artificial intelligence (AI).
    Asked in 3 papers2024-25 KME074 · 2023-24 KME074 · 2022-23 KME074
    Also asked as:
    • Explain the terms Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning.
    • Differentiate between Artificial Intelligence (AI) and Machine Learning (ML), highlighting their key distinctions.
  2. Explain the difference between reinforcement learning and deep learning.
    Asked in 3 papers2023-24 KOE073 · 2023-24 KME074 · 2022-23 KME074
    Also asked as:
    • What is Reinforcement Learning?
  3. Define K-Means Clustering.
    Asked in 2 papers2023-24 KME074 · 2022-23 KME074
    Also asked as:
    • How does K-Means Clustering work in unsupervised learning?
  4. Differentiate between Training data and Testing Data in ML
    Asked in 2 papers2024-25 KME074 · 2022-23 KME074
    Also asked as:
    • Differentiate between training data and testing data in machine learning.
  5. Explain the Multidimensional Scaling.
    Asked in 2 papers2023-24 KME074 · 2022-23 KME074
  6. Explain the meaning of reproduction in genetic algorithm
    Asked in 2 papers2023-24 KME074 · 2022-23 KME074
  7. What is Bayes theorem?
    Asked in 2 papers2024-25 KOE073 · 2022-23 KOE073
  8. What is mistake bound model of learning?
    Asked in 2 papers2024-25 KOE073 · 2023-24 KOE073
    Also asked as:
    • What is the mistake bound model of learning?

Unit-wise questions from recent papers

Section C questions from the latest papers (2024-25 KOE073, 2024-25 KME074, 2023-24 KOE073), sorted by unit. Use these to practise one unit at a time.

Unit 1
  • What are the essential steps involved in designing a learning system? Explain each step briefly. (2024-25, KME074)
  • Explain the applications of machine learning in mechanical engineering. Provide at least three use cases. (2024-25, KME074)
  • Explain the working steps of the Find-S algorithm. (2024-25, KOE073)
  • Explain how the List-Then-Eliminate algorithm finds consistent hypotheses. (2024-25, KOE073)
  • Explain the concept of inductive bias in machine learning, and provide an 10 example to illustrate how it influences the learning process. (2023-24, KOE073)
  • Explain the Candidate Elimination algorithm in machine learning and provide a 10 step-by-step solution for a hypothetical scenario. (2023-24, KOE073)
Unit 2
  • Explain Bayesian Decision Theory. (2024-25, KME074)
  • What are bias and variance in machine learning? How do they affect model performance? (2024-25, KME074)
  • Demonstrate the training of a perceptron using the perceptron learning rule. (2024-25, KOE073)
  • Demonstrate how to update weights using the Delta rule. (2024-25, KOE073)
  • What is the difference between forward propagation and backward propagation 10 in neural networks explain weight calculation for forward pass network? (2023-24, KOE073)
  • Consider a single-layer neural network with one input neuron, one hidden 10 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 training using the given data and update the weights and biases. (2023-24, KOE073)
Unit 3
  • Define Principal Component Analysis (PCA) and its role in dimensionality reduction. (2024-25, KME074)
  • What is clustering, and how does it differ from classification? (2024-25, KME074)
  • Explain the steps of the EM algorithm: Expectation step and Maximization step. (2024-25, KOE073)
  • Describe how conditional probabilities are represented in Bayesian Belief Networks (2024-25, KOE073)
  • Suppose we have a dataset of weather conditions and corresponding play 10 decisions (play or not play) as follows: Day Weather Temperatu Humidity Windy Play re 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? (2023-24, KOE073)
  • Suppose there is a rare disease that affects 1 in 10,000 people. A diagnostic test 10 for this disease has a 99% accuracy rate for both true positives (correctly identifying a person with the disease) and true negatives (correctly identifying a person without the disease). If a randomly selected person takes the test and it comes back positive, what is the probability using bayes theorem that they actually have the disease? (2023-24, KOE073)
Unit 4
  • Given stride S and kernel sizes for each layer of a (1-dimensional) CNN, create a function to compute the receptive field of a particular node in the network. This is just finding how many input nodes actually connect through to a neuron in a CNN. (2024-25, KME074)
  • Explain the concept of a kernel function in machine learning. How does it enable Support Vector Machines (SVM) to perform classification in higher-dimensional spaces? Provide examples of three commonly used kernel functions and their applications. (2024-25, KME074)
  • Demonstrate the application of the mistake bound model to the Perceptron learning algorithm. (2024-25, KOE073)
  • Demonstrate the application of LWR to a simple regression dataset. (2024-25, KOE073)
  • What is mistake bound model of learning? Explain the concept, algorithm 10 evaluation, its benefit and limitation. (2023-24, KOE073)
  • Suppose you have a dataset with the following points in a 2-dimensional space: 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. (2023-24, KOE073)
Unit 5
  • Compare and contrast Bayesian estimation with Maximum Likelihood Estimation (MLE). Highlight their advantages and disadvantages in the context of machine learning models. (2024-25, KME074)
  • Discuss the differences between reinforcement learning and deep learning in the context of problem-solving. (2024-25, KME074)
  • Explain the key components of a reinforcement learning framework: states, actions, rewards. (2024-25, KOE073)
  • Describe the significance of the learning rate and discount factor in Q-learning. (2024-25, KOE073)
  • Consider a binary string optimization problem where the goal is to evolve a 10 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. (2023-24, KOE073)
  • What are the different types of reinforcement? Explain. (2023-24, KOE073)

Papers used for this list

How to use these questions

  1. Start with the questions asked in the most papers, and make short notes for each.
  2. Write 2–3 long answers by hand with a timer (about 12–15 minutes each), then compare them with your textbook.
  3. Revise all the 2-mark questions the night before the exam.
  4. Finish with one full paper from the list above, timed.

Full plan with AI prompts: How to prepare for AKTU exams using previous year papers. After results, check your marks with the AKTU CGPA calculator.

Frequently asked questions

Are these AKTU Machine Learning (Open Elective) questions guaranteed to come in the exam?
No. They are the questions that repeated most in past papers, which makes them a strong hint, but AKTU can ask anything from the syllabus. Use this list to prioritise, not to skip units.

How was this important questions list made?
We compared all 150 questions from 6 AKTU Machine Learning (Open Elective) papers (2022-23 to 2024-25) and grouped questions that ask about the same topic, then counted how many papers each appeared in.

Where can I download AKTU Machine Learning (Open Elective) previous year papers?
Every paper used here is on LearnAIFast with all questions as text and a link to the original PDF. See the list of papers above.

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