We Discuss About That NPTEL IITKGP Introduction to Machine Learning Assignment 4 Answers
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NPTEL IITKGP Introduction to Machine Learning
ABOUT THE COURSE :
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.
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.
- 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
UNPROCTORED PROGRAMMING EXAM SCORE >=10/25 AND PROCTORED EXAM SCORE >= 20/50.
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1. A man is known to speak the truth 2 out of 3 times. He throws a die and reports that the number obtained is 4. Find the probability that the number obtained is actually 4:
a. 2/3
b. 3/4
c. 5/22
d. 2/7 .
Answer:- d
2. Consider the following graphical model, mark which of the following pair of random variables are independent given no evidence?
a. a,b
b. c,d
c. e,d
d. C,e
Answer:- a
NPTEL Introduction To Machine Learning – IITKGP Assignment 4 Answers
3. Two cards are drawn at random from a deck of 52 cards without replacement. What is the probability of drawing a 2 and an Ace in that order?
a. 4/51
b. 1/13
c. 4/256
d. 4/663
Answer:- d
4. Consider the following Bayesian network. The random variables given in the model are modeled as discrete variables (Rain = R, Sprinkler = S and Wet Grass = W) and the corresponding probablity values are given below.
Calculate P(S |W, R).
a. 1
b. 0.5
c. 0.22
c. 0.78
Answer:- c
5. What is the naive assumption in a Nave Bayes Classitier?
A. All the classes are independent of each other
B. All the features of a class are independent of each other
C. The most probable feature for a class is the most important feature to be considered for classification
D. All the features of a class are conditionally dependent on each other.
Answer:- b
6. A drug test (random variable 1) has 1% false positives (1.e., 1% of those not taking drugs show positive in the test). and 5% false negatives (i.e., 5% of those taking drugs test negative). Suppose that 2% of those tested are taking drugs. Determine the probability that somebody who tests positive is actually taking drugs (random variable D).
A. 0.66
B. 0.34
C. 0.50
D. 0.91
Answer:- a
NPTEL Introduction To Machine Learning – IITKGP Assignment 4 Answers
7. It is given that P(A]B) = 2/3 and P(A|B) = 1/4. Compute the value of P (B|A).
A. 1/2
B. 2/3
C. 3/4
D. Not enough information.
Answer:- a
8. What is the joint probability distribution in terms of conditional probabilities?
A. P(D1) P(D2|D1)* P(S1|D1) * P($2|D1) * P(S3|D2)
B. P(D1) * P(D2) * P(S1|D1) * P($2|D1) * P($3|D1, D2)
C. P(D1) P(D2) * P(S1|D2) * P(S2|D2) * P($3|D2)
D. P(D1) * P(D2) * P(S1|D1) * P($2|D1, D2) * P($3|D2)
Answer:- d
9. Suppose P(DI) = 0.5, P(D2)=0.6, P(S1D1)=0.4 and P(S1| DI’)=0.6. Find P(S1)
A. 0.14
B. 0.36
C. 0.50
D. 0.66
Answer:- b
10. In a Bayesian network a node with only Outgoing edge(s) represents
A. a variable conditionally independent of the other variables.
B. a variable dependent on its silings.
C. a variable whose dependency is uncertain.
D. None of the above.
Answer:- a
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