**NPTEL Introduction to Machine Learning – IITKGP Assignment 3 Answers 2023:- **In this post, We have provided answers of NPTEL Introduction to Machine Learning – IITKGP Assignment 3 Week 3. We provided answers here only for reference. Plz, do your assignment at your own knowledge.

## NPTEL Introduction To Machine Learning – IITKGP Week 3 Assignment Answer 2023 July 2023

**Q1. Fill in the blanks: **K-Nearest Neighbor is a

a. Non-parametric, eager

b. Parametric, eager

c. Non-parametric, lazy

d. Parametric, lazy algorithm

Answer :-c

2. You have been given the following 2 statements. Find out which of these options is/are true in the case of k-NN.

(i) In case of very large value of k, we may include points from other classes into the neighborhood.

(ii) In case of too small value of k, the algorithm is very sensitive to noise.

a. (i) is True and (ii) is False

b. (i) is False and (ii) is True

c. Both are True

d. Both are False

Answer :-c

3. State whether the statement is True/False: k-NN algorithm does more computation on test time rather than train time.

a. True

b. False

Answer :-For Answer Click Here

4. Suppose you are given the following images (1 represents the left image, 2 represents the middle and 3 represents the right). Now your task is to find out the value of k in k-NN in each of the images shown below. Here k1 is for 15, k2 is for 2nd and k3 is for 3rd figure.

a. k1 > k2> k3

b. k1 < k2> k3

c. k1 < k2 < k3

d. None of these

Answer :-

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

a. Irrelevant and redundant features

b. Limited training data

c. Limited computational resources.

d. All of the above

Answer :-For Answer Click Here

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

Answer :-For Answer Click Here

7. What is the optimum number of principal components in the below figure?

a. 10

b. 20

c. 30

d. 40

Answer :-

8. Suppose we are using dimensionality reduction as pre-processing technique, i.e, instead of using all the features, we reduce the data to k dimensions with PCA. And then use these PCA projections as our features. Which of the following statements is correct? Choose which of the options is correct?

a. Higher value of ‘k’ means more regularization

b. Higher value of ‘K‘ means less regularization

Answer :-

9. In 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 :-

10. The major limitation of collaborative filtering is:

a. Cold start

b. Overspecialization

c. None of the above

Answer :-For Answer Click Here

11. Consider the figures below. Which figure shows the most probable PC component directions for the data points?

Answer :-

12. Suppose that you wish to reduce the number of dimensions of a given data to dimensions using PCA. Which of the following statement is correct?

a. Higher means more regularization

b. Higher means less regularization

c. Can’t Say

Answer :-For Answer Click Here

13. Suppose you are given 7 plots 1-7 (left to right) and you want to compare Pearson correlation coefficients between variables of each plot. Which of the following is true?

Answer :-

14. Imagine you are dealing with 20 class classification problem. What is the maximum number of discriminant vectors that can be produced by LDA?

a. 20

b. 19

c. 21

d. 10

Answer :-

15. In which of the following situations collaborative filtering algorithm is appropriate?

a. You manage an online bookstore and you have the book ratings from many users. For each user, you want to recommend other books he/she will like based on her previous ratings and other users’ ratings.

b. You manage an online bookstore and you have the book ratings from many users. You want to predict the expected sales volume (No of books sold) as a function of average rating of a book.

c. Both A and B

d. None of the above

Answer :-For Answer Click Here

## NPTEL Introduction to Machine Learning – IITKGP Assignment 3 Answers [July 2022]

**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: K–Nearest 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

👇**For Week 04 Assignment Answers**👇

**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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## About Introduction To Machine Learning – IITKGP

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.

COURSE LAYOUT

**Week 1:**Introduction: Basic definitions, types of learning, hypothesis space and inductive bias, evaluation, cross-validation**Week 2:**Linear regression, Decision trees, overfitting**Week 3:**Instance based learning, Feature reduction, Collaborative filtering based recommendation**Week 4:**Probability and Bayes learning**Week 5:**Logistic Regression, Support Vector Machine, Kernel function and Kernel SVM**Week 6:**Neural network: Perceptron, multilayer network, backpropagation, introduction to deep neural network**Week 7:**Computational learning theory, PAC learning model, Sample complexity, VC Dimension, Ensemble learning**Week 8:**Clustering: k-means, adaptive hierarchical clustering, Gaussian mixture model

**CRITERIA TO GET A CERTIFICATE**

Average assignment score = 25% of average of best 6 assignments out of the total 8 assignments given in the course.

Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

**YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.**

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