# NPTEL Introduction to Machine Learning Assignment 11 Answer

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NPTEL Introduction to Machine Learning Assignment 11 Answer – 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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## NPTEL Introduction to Machine Learning Assignment

ABOUT THE COURSE :
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.
INTENDED AUDIENCE : This is an elective course. Intended for senior UG/PG students. BE/ME/MS/PhD
PREREQUISITES : We will assume that the students know programming for some of the assignments.If the students have done introductory courses on probability theory and linear algebra it would be helpful. We will review some of the basic topics in the first two weeks as well.
INDUSTRY SUPPORT : Any company in the data analytics/data science/big data domain would value this course

Next Week Assignment Answers

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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1 point

What are the necessary conditions for
P(xn)=1kπkP(xn|θk)P(xn)=∑1kπkP(xn|θk)
to be a density? (Select all that apply)

Ans   – C,D
Based on the density estimation of a GMM given below, answer questions 24.Based on the density estimation of a GMM given below, answer questions 2−4.

1 point
What is value of k?

Ans   – C
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1 point

What is the minimum value of kkk′≠k, where kk is from previous question, for which you will get a very similar density estimation?

Ans   – C
2 points

Assume equal πiπi for each gaussian model after convergence as in Q2. What would (approximately) be πiπi’s for the model you’ll get with kk′ as in Q3?

Ans   -B
For a set of points (given in orange), the density estimation of a GMM is given below. Based on this, answer questions 5 and 6.For a set of points (given in orange), the density estimation of a GMM is given below. Based on this, answer questions 5 and 6.

1 point
What is the problem evident in the image?

Ans   – A
1 point
What can be done to get a better fit?

Ans   – B
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1 point
What does soft clustering mean in GMMs?

Ans   – C
1 point

What is the update for πkπk in EM algorithm for GMM?

Ans   – A

1 point
KNN is a special case of GMM with the following properties: (Select all that apply)

Ans   – All
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