# NPTEL Introduction To Machine Learning – IITKGP Assignment 3 Answes

NPTEL Introduction to Machine Learning – IITKGP Assignment 3 Answers 2022:- 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 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`

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`

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`

## 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.