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NPTEL Introduction to Machine Learning Assignment 3 Answer

We Discuss About That NPTEL Introduction to Machine Learning Assignment 3 Answer

NPTEL Introduction to Machine Learning Assignment 2 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. For linear classification we use:

a. A linear function to separate the classes.
b. A linear function to model the data.
c. A linear loss.
d. Non-linear function to fit the data.

Answer:- b

2. Logit transformation for Pr(X=1) for given data is S=[0,1,1,0,1,0,1]
a. 3/4
b. 4/3
c. 4/7
d. 3/7

Answer:- c
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3. The output of binary class logistic regression lies in this range.

a. [−∞,∞] b. [−1,1] c. [0,1] d. [−∞,0]

Answer:- d

4. If log(1−p(x)1+p(x))=β0+βxlog What is p(x)p(x)?

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5. Logistic regression is robust to outliers. Why?

a. The squashing of output values between [0, 1] dampens the affect of outliers.
b. Linear models are robust to outliers.
c. The parameters in logistic regression tend to take small values due to the nature of the problem setting and hence outliers get translated to the same range as other samples.
d. The given statement is false.

Answer:- d

6. Aim of LDA is (multiple options may apply)

a. Minimize intra-class variability.
b. Maximize intra-class variability.
c. Minimize the distance between the mean of classes
d. Maximize the distance between the mean of classes

Answer:-B
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7. We have two classes in our dataset with mean 0 and 1, and variance 2 and 3.

a. LDA may be able to classify them perfectly.
b. LDA will definitely be able to classify them perfectly.
c. LDA will definitely NOT be able to classify them perfectly.
d. None of the above.

Answer:- a

8. We have two classes in our dataset with mean 0 and 5, and variance 1 and 2.

a. LDA may be able to classify them perfectly.
b. LDA will definitely be able to classify them perfectly.
c. LDA will definitely NOT be able to classify them perfectly.
d. None of the above.

Answer:- b

9. For the two classes ’+ and ’-’ shown below.

While performing LDA on it, which line is the most appropriate for projecting data points?

a. Red
b. Orange
c. Blue
d. Green

Answer:- b

10. LDA assumes that the class data is distributed as:

a. Poisson
b. Uniform
c. Gaussian
d. LDA makes no such assumption.

Answer:- D
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