NPTEL Introduction to Machine Learning Assignment 3 Answers 2022

Are you looking for the Answers to NPTEL Introduction to Machine Learning Assignment 3 – IIT Madras? This article will help you with the answer to the National Programme on Technology Enhanced Learning (NPTEL) Course “NPTEL Introduction to Machine Learning Assignment 3

What is Introduction to Machine Learning?

With the increased availability of data from varied sources there has been increasing attention paid to the various data driven disciplines such as analytics and machine learning. In this course we intend to introduce some of the basic concepts of machine learning from a mathematically well motivated perspective. We will cover the different learning paradigms and some of the more popular algorithms and architectures used in each of these paradigms.

CRITERIA TO GET A CERTIFICATE

Average assignment score = 25% of the average of best 8 assignments out of the total 12 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 THE 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.

Below you can find the answers for NPTEL Introduction to Machine Learning Assignment 3

NPTEL Introduction to Machine Learning Assignment 3 Answers:-

Q1. consider the case where two classes follow Gaussian distribution which are centered at (6, 8) and (−6, −4) and have identity
covariance matrix. Which of the following is the separating decision boundary using LDA assuming the priors to be equal?

Q2. Which of the following are differences between PCR and LDA?

Q3. Which of the following are differences between LDA and Logistic Regression?

Q4. We have two classes in our dataset. The two classes have the same mean but different variance.

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Q5. We have two classes in our dataset. The two classes have the same variance but different mean.

Q6. Which of these techniques do we use to optimise Logistic Regression:

Q7. Suppose we have two variables, X and Y (the dependent variable), and we wish to find their relation. An expert tells us that relation
between the two has the form Y=meX+c. Suppose the samples of the variables X and Y are available to us. Is it possible to apply
linear regression to this data to estimate the values of m and c?

Q8. What might happen to our logistic regression model if the number of features is more than the number of samples in our dataset?

Q9. Logistic regression also has an application in

Q10. Consider the following datasets: