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NPTEL IITKGP Introduction to Machine Learning Assignment 3 Answers

We Discuss About That NPTEL IITKGP Introduction to Machine Learning Assignment 3 Answers

NPTEL IITKGP Introduction to Machine Learning Assignment 3 Answers – Here All The Questions and Answers Provided to Help All The Students and NPTEL Candidate as a Reference Purpose, It is Mandetory to Submit Your Weekly Assignment By Your Own Understand Level.

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Table of Contents

NPTEL IITKGP Introduction to Machine Learning

ABOUT THE COURSE :

This course provides a concise introduction to the fundamental concepts in machine learning and popular machine learning algorithms. We will cover the standard and most popular supervised learning algorithms including linear regression, logistic regression, decision trees, k-nearest neighbour, an introduction to Bayesian learning and the naïve Bayes algorithm, support vector machines and kernels and neural networks with an introduction to Deep Learning. We will also cover the basic clustering algorithms. Feature reduction methods will also be discussed. We will introduce the basics of computational learning theory. In the course we will discuss various issues related to the application of machine learning algorithms. We will discuss hypothesis space, overfitting, bias and variance, tradeoffs between representational power and learnability, evaluation strategies and cross-validation. The course will be accompanied by hands-on problem solving with programming in Python and some tutorial sessions.

Next Week Assignment Answers

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This course can have Associate in Nursing unproctored programming communication conjointly excluding the Proctored communication, please check announcement section for date and time. The programming communication can have a weightage of twenty fifth towards the ultimate score.

Final score = Assignment score + Unproctored programming exam score + Proctored Exam score
  • Assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.
  • ( All assignments in a particular week will be counted towards final scoring – quizzes and programming assignments). 
  • Unproctored programming exam score = 25% of the average scores obtained as part of Unproctored programming exam – out of 100
  • Proctored Exam score =50% of the proctored certification exam score out of 100
YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF ASSIGNMENT SCORE >=10/25 AND
UNPROCTORED PROGRAMMING EXAM SCORE >=10/25 AND PROCTORED EXAM SCORE >= 20/50. 
If any one of the 3 criteria is not met, you will not be eligible for the certificate even if the Final score >= 40/100. 

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Q1. Suppose, you have given the following data where x and y are the 2 input variables and Class is the dependent variable.

Suppose, you want to predict the class of new data point x=1 and y=1 using euclidean distance in 3-NN. To which class the new data point belongs to?

A. +Class
B. – Class
C. Can’t say
D. None of these

Answer:- b

2. Imagine you are dealing with a 10 class classification problem. What is the maximum number of discriminant vectors that can be produced by LDA?

A. 20
B. 14
C. 9
D. 10

Answer:- c
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3. Fill in the blanks: KNearest Neighbor is a_ algorithm

A. Non-parametric, eager
B. Parametric, eager
C. Non-parametric, lazy
D. Parametric, lazy

Answer:- c

4. Which of the following statements is True about the KNN algorithm?

A. KNN algorithm does more computation on test time rather than train time.
B. KNN algorithm does lesser computation on test time rather than train time.
C. KNN algorithm does an equal amount of computation on test time and train time.
D. None of these.

Answer:- a

5. Which of the following necessitates feature reduction in machine learning?

A. Irrelevant and redundant features
B. Curse of dimensionality
C. Limited computational resources.
D. All of the above

Answer:- d

6. When there is noise in data, which of the following options would improve the perfomance of the KNN algorithm?

A. Increase the value of k
B. Decrease the value of k
C. Changing value of k will not change the effect of the noise D. None of these

Answer:- a
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7. Find the value of the Pearson’s correlation coefficient of X and Y from the data in the following table.

A. 0.47
B. 0.68
C. 1
D. 0.33

Answer:- b

8. Which of the following is false about PCA?

A. PCA is a supervised method
B. It identifies the directions that data have the largest variance
C. Maximum number of principal components = number of features
D. All principal components are othogonal to each other

Answer:- a

9. In user-based collaborative filtering based recommendation, the items are recommended based on :

A. Similar users
B. Similar items
C. Both of the above
D. None of the above

Answer:- a

10. Identify whether the following statement is true or false? “PCA can be used for projecting and visualizing data in lower dimensions.

A. TRUE
B. FALSE

Answer:- a
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