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NPTEL Introduction to Machine Learning Assignment 4 Answer

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NPTEL Introduction to Machine Learning Assignment 4 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

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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. Consider the 1-dimensional dataset:

State true or false: The dataset becomes linearly separable after using basis expansion with the following basis function ϕ(x)=[1×3]ϕ(x)=[1×3]

NPTEL Introduction to Machine Learning Assignment 4 Answers
a. True
b.False
Answer:- A
2. Consider a linear SVM trained with nn labeled points in R2R2 without slack penalties and resulting in k=2k=2 support vectors, where n>100n>100. By removing one labeled training point and retraining the SVM classifier, what is the maximum possible number of support vectors in the resulting solution?

a. 1
b. 2
c. 3
d. n − 1
e. n

Answer:- B

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NPTEL Introduction to Machine Learning Assignment 4 Answers
3. Which of the following are valid kernel functions?

a. (1+<x,x’>)d(1+<x,x′>)d
b. tanh(K1<x,x’>+K2)
c. exp(−γ||x−x’||2)

Answer:- A,B,C
4. Consider the following dataset:

NPTEL Introduction to Machine Learning Assignment 4 Answers
Which of these is not a support vector when using a Support Vector Classifier with a polynomial kernel with degree =3,C=1,=3,C=1, and gamma =0.1?=0.1?
(We recommend using sklearn to solve this question.)

a. 3
b.1
c. 9
d. 10

Answer:- B
5. Consider an SVM with a second order polynomial kernel. Kernel 1 maps each input data point xx to K1(x)=[x x2]. Kernel 2 maps each input data point xx to K2(x)=[3x 3×2]K2(x). Assume the hyper-parameters are fixed. Which of the following option is true?

a. The margin obtained using K2(x)K2(x) will be larger than the margin obtained using K1(x)K1(x).
b. The margin obtained using K2(x)K2(x) will be smaller than the margin obtained using K1(x)K1(x).
c. The margin obtained using K2(x)K2(x) will be the same as the margin obtained using K1(x)K1(x).

Answer:- C
6. Train a Linear perceptron classifier on the modified iris dataset. Report the best classification accuracy for l1 and elasticnet penalty terms.

(We recommend using sklearn.)

a. 0.82, 0.64
b. 0.90, 0.71
c. 0.84, 0.82
d. 0.78, 0.64

Answer:- B

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NPTEL Introduction to Machine Learning Assignment 4 Answers
7. Train an SVM classifier on the modified iris dataset. We encourage you to explore the impact of varying different hyperparameters of the model. Specifically, try different kernels and the associated hyperparameters. As part of the assignment, train models with the following set of hyperparameters
poly, gamma=0.4gamma=0.4, one-vs-rest classifier, no-feature-normalization.

a. 0.98
b. 0.96
c. 0.92
d. 0.94

Answer:- A

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