2023 question paper
MACHINE LEARNING
21 questions
Q1a. (a) Who is called the father of Machine Learning? (i) Geoffrey Hill (ii) Geoffrey Chaucer (iii) Geoffrey Everest Hinton (iv) Tom Mitchell20232m
Module 1: Introduction
View this question on its own page →(a) Who is called the father of Machine Learning?
(i) Geoffrey Hill
(ii) Geoffrey Chaucer
(iii) Geoffrey Everest Hinton
(iv) Tom MitchellQ1b. (b) A Machine Learning technique that helps in detecting the outliers in data is called as: - (i) Clustering . (ii) Classification (iii) Anomaly Detection (iv) All of the above20232m
Module 4: Clustering, K-means, K-medoids, Density-based Hierarchical, Spectral .
View this question on its own page →(b) A Machine Learning technique that helps in detecting the outliers in data is called as:
- (i) Clustering .
(ii) Classification
(iii) Anomaly Detection
(iv) All of the above
- (i) Clustering .
Q1c. \(c\) In what type of learning labelled training data is used : (i) unsupervised learning (ii) supervised learning (iii) reinforcement learning (iv) active learning20232m
Module 1: Introduction
View this question on its own page →(c) In what type of learning labelled training data is used :
(i) unsupervised learning
(ii) supervised learning
(iii) reinforcement learning
(iv) active learningQ1g. What is a support vector? (i) The distance between any two data points (ii) The average distance between all data points (iii) The distance between any two boundary data points . (iv)The minimum distance between any two data points20232m
Module 3: Classification Algorithms
View this question on its own page →What is a support vector?
(i) The distance between any two data points
(ii) The average distance between all data points
(iii) The distance between any two boundary data points .
(iv)The minimum distance between any two data pointsQ1h. In neural network. which of the following techniques is used to deal with over fitting? × (i) Dropout (ii) Regularization. (iii) Batch normalization (iv) All of the above20232m
Module 3: Classification Algorithms
View this question on its own page →In neural network. which of the following techniques is used to deal with over fitting? ×
(i) Dropout
(ii) Regularization.
(iii) Batch normalization
(iv) All of the aboveQ1i. A hypothesis which defines the population distribution is called .... (i) Null Hypothesis . (ii) Positive Hypothesis (iii) Negative Hypothesis (iv) Alternative Hypothesis20232m
Module 4: Clustering, K-means, K-medoids, Density-based Hierarchical, Spectral .
View this question on its own page →A hypothesis which defines the population distribution is called ....
(i) Null Hypothesis .
(ii) Positive Hypothesis
(iii) Negative Hypothesis
(iv) Alternative HypothesisQ1j. Which of the following is the best algorithm for text classification? (i) KNN - (ii) Decision tree (iii) Random forest (iv) Naive Bayes20232m
Module 3: Classification Algorithms
View this question on its own page →Which of the following is the best algorithm for text classification?
(i) KNN -
(ii) Decision tree
(iii) Random forest
(iv) Naive BayesQ2a. Q.2 (a) What are the important objectives of machine learning?20237m
Module 1: Introduction
View this question on its own page →Q.2 (a) What are the important objectives of machine learning?
Q2b. Q.2 (b) Explain the inductive biased hypothesis space.20237m
Module 1: Introduction
View this question on its own page →Q.2 (b) Explain the inductive biased hypothesis space.
Q3a. What is Linear Regression? Explain in detail using example and list all the assumption to be met before starting with linear regression.20237m
Module 2: Linear Models and Bayesian Learning
View this question on its own page →What is Linear Regression? Explain in detail using example and list all the assumption to be met before starting with linear regression.
Q3b. How does Back Propagation Rule help in learning in Neural Network?20237m
Module 3: Classification Algorithms
View this question on its own page →How does Back Propagation Rule help in learning in Neural Network?
Q4a. Discuss various issues in Decision Tree Learning.20237m
Module 3: Classification Algorithms
View this question on its own page →Discuss various issues in Decision Tree Learning.
Q4b. Discuss with examples of instance-based learning algorithms.20237m
Module 3: Classification Algorithms
View this question on its own page →Discuss with examples of instance-based learning algorithms.
Q5a. State the mathematical formulation of the SVM problem. Give an outline of the method for solving the problem.20237m
Module 3: Classification Algorithms
View this question on its own page →State the mathematical formulation of the SVM problem. Give an outline of the method for solving the problem.
Q5b. A patient takes a lab test and the result comes back positive. It is known that the test returns a correct positive result in only 98% of the cases and a correct negative result in only 97% of the cases. Furthermore, only 0.008 of the entire population has this disease. (i) What is the probability that this patient has cancer? (ii) What is the probability that he does not have cancer? (iii) What is the diagnosis?20237m
Module 2: Linear Models and Bayesian Learning
View this question on its own page →A patient takes a lab test and the result comes back positive. It is known that the test returns a correct positive result in only 98% of the cases and a correct negative result in only 97% of the cases. Furthermore, only 0.008 of the entire population has this disease.
(i) What is the probability that this patient has cancer?
(ii) What is the probability that he does not have cancer?
(iii) What is the diagnosis?Q6. For the Sun Burn data set given below, construct a decision tree. | Name | Hair | Height | Weight | Location | Class | |--------|--------|----------|---------|----------|-------| | Sunita | Blonde | Average | Light | No | Yes | | Anita | Blonde | Tall | Average | Yes | No | | Kavita | Brown | Short | Average | Yes | No | | Sushma | Blonde | Short | Average | No | Yes | | Xavier | Red | Average | Heavy | No | Yes | | Balaji | Brown | Tall | Heavy | No | No | | Ramesh | Brown | Average | Heavy | No | No | | Sweta | Blonde | Short | Light | Yes | No |202314m
Module 3: Classification Algorithms
View this question on its own page →For the Sun Burn data set given below, construct a decision tree.
Name Hair Height Weight Location Class Sunita Blonde Average Light No Yes Anita Blonde Tall Average Yes No Kavita Brown Short Average Yes No Sushma Blonde Short Average No Yes Xavier Red Average Heavy No Yes Balaji Brown Tall Heavy No No Ramesh Brown Average Heavy No No Sweta Blonde Short Light Yes No Q7a. Discuss the Algorithmic convergence & Generalization property of ANN.20237m
Module 3: Classification Algorithms
View this question on its own page →Discuss the Algorithmic convergence & Generalization property of ANN.
Q7b. What are the applications of supervised machine learning in modern business?20237m
Module 3: Classification Algorithms
View this question on its own page →What are the applications of supervised machine learning in modern business?
Q8a. Write short notes on Probably Approximately Correct (PAC) learning model.20237m
Module 5: Probabilistic & Reinforcement Methods
View this question on its own page →Write short notes on Probably Approximately Correct (PAC) learning model.
Q8b. What is the difference between K-NN and K-means clustering algorithms?20237m
Module 4: Clustering, K-means, K-medoids, Density-based Hierarchical, Spectral .
View this question on its own page →What is the difference between K-NN and K-means clustering algorithms?
Q9. Write short notes on any two of the following: (a) Naive Bayes (b) Logistic Regression \(c\) Back Propagation algorithm202314m
Module 3: Classification Algorithms
View this question on its own page →Write short notes on any two of the following:
(a) Naive Bayes
(b) Logistic Regression
(c) Back Propagation algorithm