# NPTEL Introduction to Machine Learning – IITKGP Assignment 2 Answers

NPTEL Introduction to Machine Learning – IITKGP Assignment 2 Answers 2022:- In this post, We have provided answers of NPTEL Introduction to Machine Learning – IITKGP Assignment 2. We provided answers here only for reference. Plz, do your assignment at your own knowledge.

## NPTEL Introduction to Machine Learning – IITKGP Assignment 2 Answers [July 2022]

1. In a binary classification problem, out of 30 data points 12 belong to class I and 18 belong to class II. What is the entropy of the data set?

A. 0.97
B 0
C. 1
D. 0.67

`Answer:- a`

2. Decision trees can be used for the problems where

A. the attributes are categorical.
B. the attributes are numeric valued.
C. the attributes are discrete valued.
D. In all the above cases.

`Answer:- d`

3. Which of the following is false?

A. Variance is the error of the trained classifier with respect to the best classifier in the concept class.
B. Variance depends on the training set size.
C. Variance increases with more training data.
D. Variance increases with more complicated classifiers.

`Answer:- c`

4. In linear regression, our hypothesis is h (x) = 6+ 0x, the training data is given in the table. A

What is the value of J(0) when 6 = (1,1).

A. 0
B. 1
C. 2
D. 0.5

`Answer:- b`

5. The value of information gain in the following decision tree is:

A. 0.380
B. 0.620
C. 0.190
D. 0.477

`Answer:- a`

6. What is true for Stochastic Gradient Descent?

A. In every iteration, model parameters are updated for multiple training samples
B. In every iteration, model parameters are updated for one training sample
C. In every iteration, model parameters are updated for all training samples
D. None of the above

`Answer:- b`

7. The entropy of the entire dataset is

A. 0.5
B. 1
C. 0
D. 0.1

`Answer:- b`

8. Which attribute will be the root of the decision tree?

A. Green
B. Legs
C. Height
D. Smelly

`Answer:- b`

9. In Linear Regression the output is:

A. Discrete
B. Continuous and always lies in a finite range
C. Continuous
D. May be discrete or continuous

`Answer:- c`

10. Identify whether the following statement is true or false? Overfitting is more likely when the set of training data is small

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.