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Introduction to Machine Learning NPTEL Week 3 Solutions NPTEL 2023

This set of MCQ(multiple choice questions) focuses on the Introduction to Machine Learning NPTEL Week 3 Solutions NPTEL 2023 .

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.

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Introduction to Machine learning NPTEL 2023 Week 3 Solutions

Q1. Fill in the blanks: K-Nearest Neighbor is a _______, ________ algorithm

a) Non-parametric, eager b) Parametric, eager c) Non-parametric, lazy d) Parametric, lazy

Q2. You have been given the following 2 statements. Find out which of these options is/are true in the case of k-NN. (i) In case of very large value of k, we may include points from other classes in to the neighborhood. (ii) In case of too small value of k, the algorithm is very sensitive to noise.

a) (i) is True and (ii) is False b) (i) is False and (ii) is True c) Both are True d) Both are False

Q3. State whether the statement is True/False: k-NN algorithm does more computation on test time rather than train time.

a) True b) False

Introduction to Machine Learning NPTEL Week 3 Solutions

Q4. Suppose you are given the following images(1 represents the left image, 2 represents the middle and 3 represents the right). Now you task is to find out the value of k in k-NN in each of the images shown below. Here k1 is for 1st, k2 is for 2nd and k3 is for 3rd figure.

a) k1 > k2 > k3 b) k1 < k2 > k3 c) k1 < k2 < k3 d) None of these

Q5. Which of the following necessitates feature reduction in machine learning?

a) Irrelevant and redundant features b) Limited training data c) Limited computational resources d) All of the above

Q6. Suppose, you have given the following data where x and y are the 2 input variables and Class is the dependent variable.

a) + Class b) – Class c) Can’t say d) None of these

Q7. What is the optimum number of principal components in the below figure?

a) 10 b) 20 c) 30 d) 40

Q8. Suppose we are using dimensionality reduction as pre-processing technique, i.e, instead of using all the features, we reduce the data to k dimensions with PCA. And then use these PCA projections as our features. Which of the following statements is correct? Choose which of the options is correct?

a) Higher value of ‘k’ means more regularization b) Higher value of ‘k’ means less regularization

Q9. In collaborative filtering-based recommendation, the items are recommended based on:

a) Similar users b) Similar items c) Both of the above d) None of the above

Q10. The major limitation of collaborative filtering is:

a) Cold start b) Overspecialization c) None of the above

Q11. Consider the figures below. Which figure shows the most probable PCA component directions for the data points?

a) A b) B c) C d) D

Q12. Suppose that you wish to reduce the number of dimensions of a given data to dimensions using PCA. Which of the following statement is correct?

a) Higher means more regularization b) Higher means less regularization c) Can’t say

Q13. Suppose you are given 7 plots 1-7 (left to right) and you want to compare Pearson correlation coefficients between variables of each plot. Which fo the following is true? 1. 1 < 2<3<4 2. 1>2>3>4 3. 7<6<5<4 4. 7>6>5>4

a) 1 and 3 b) 2 and 3 c) 1 and 4 d) 2 and 4

Q14. Imagine you are dealing with 20 class classification problem. What is the maximum number of discriminant vectors that can be produced by LDA?

a) 20 b) 19 c) 21 d) 10

Q15. In which of the following situations collaborative filtering algorithm is appropriate?

a) You manage an online bookstore and you have the book ratins from many users. For each user, you want to recommend other books he/she will like based on her previous ratins and other users’ ratings. b) You manage an online bookstore and you have the book raings from many users. You want to predict the expected sales volume(No of books sold) as a function of average rating of a book. c) Both A and B d) None of the above

Q1. Which of the following is false about a logistic regression based classifier?

a)  The logistic function is non-linear in the weights b) The logistic function is linear in the weights c) he decision boundary is non-linear in the weights d) The decision boundary is linear in the weights

Answer: a,c

Q2. Consider the case where two classes follow Gaussian distribution which are centered at (3, 9) and (−3, 3) and have identity covariance matrix. Which of the following is the separating decision boundary using LDA assuming the priors to be equal?

a) y−x=3 b) x+y=3 c) x+y=6 d) both (b) and (c) e) None of the above f) Can not be found from the given information

Q3. Consider the following relation between a dependent variable and an independent variable identified by doing simple linear regression. Which among the following relations between the two variables does the graph indicate?

introduction to machine learning nptel assignment 3 answers

a)  as the independent variable increases, so does the dependent variable b) as the independent variable increases, the dependent variable decreases c) if an increase in the value of the dependent variable is observed, then the independent variable will show a corresponding increase d) if an increase in the value of the dependent variable is observed, then the independent variable will show a corresponding decrease e)  the dependent variable in this graph does not actually depend on the independent variable f) none of the above

Q4. Given the following distribution of data points:

introduction to machine learning nptel assignment 3 answers

What method would you choose to perform Dimensionality Reduction?

a) Linear Discriminant Analysis b) Principal Component Analysis

Q5. In general, which of the following classification methods is the most resistant to gross outliers?

a) Quadratic Discriminant Analysis (QDA) b) Linear Regression c) Logistic regression d) Linear Discriminant Analysis (LDA)

Q6. Suppose that we have two variables, X and Y (the dependent variable). We wish to find the relation between them. An expert tells us that relation between the two has the form Y=m+X2+c=+2+. Available to us are samples of the variables X and Y. Is it possible to apply linear regression to this data to estimate the values of m and c?

a) no b) yes c) insufficient information

Q7. In a binary classification scenario where x is the independent variable and y is the dependent variable, logistic regression assumes that the conditional distribution y|x| follows a

a) Bernoulli distribution  b) binomial distribution  c) normal distribution  d) exponential distribution

Q8. Consider the following data:

introduction to machine learning nptel assignment 3 answers

Assuming that you apply LDA to this data, what is the estimated covariance matrix?

a) [1.8750.31250.31250.9375][1.8750.31250.31250.9375] b) [2.50.41670.41671.25] c) [1.8750.31250.31251.2188] d) [2.50.41670.41671.625] e) [3.251.16671.16672.375] f) [2.43750.8750.8751.7812] g) None of these

Q9. Given the following 3D input data, identify the principal component.

introduction to machine learning nptel assignment 3 answers

(Steps: center the data, calculate the sample covariance matrix, calculate the eigenvectors and eigenvalues, identify the principal component)

a) ⎢−0.10220.00180.9948⎤⎦⎥ b) ⎡⎣⎢0.5742−0.81640.0605⎤⎦⎥  c) ⎢0.57420.81640.0605⎤⎦⎥ d) ⎡⎣⎢−0.57420.81640.0605⎤⎦ e) ⎡⎣⎢0.81230.57740.0824⎤⎦⎥ f) None of the above

Q10. For the data given in the previous question, find the transformed input along the first two principal components.

a) ⎡⎣⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢0.6100−0.4487−1.26511.33450.5474−1.0250−1.26721.5142−0.0196−0.1181−0.11630.5702−0.72570.27270.1724−0.0355⎤⎦⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥ b) ⎡⎣⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢−0.1817−1.2404−2.05680.5428−0.2443−1.8167−2.05890.72250.89440.79590.79771.48420.18841.18681.08640.8785⎤⎦⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥ c) ⎡⎣⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢−6.2814−4.3143−3.7368−1.79502.29173.52894.91865.38830.6100−0.4487−1.26511.33450.5474−1.0250−1.26721.5142⎤⎦⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥  d) ⎡⎣⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢1.47213.43924.01665.958410.045111.282312.672013.1418−0.1817−1.2404−2.05680.5428−0.2443−1.8167−2.05890.7225⎤⎦⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥ e) None of the above

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NPTEL Introduction To Machine Learning – IITKGP Assignment 3 Answers 2023

NPTEL Introduction to Machine Learning – IITKGP Assignment 3 Answers 2023:-  In this post, We have provided answers of NPTEL Introduction to Machine Learning – IITKGP Assignment 3 Week 3. We provided answers here only for reference. Plz, do your assignment at your own knowledge.

NPTEL Introduction To Machine Learning – IITKGP Week 3 Assignment Answer 2023 July 2023

Q1. Fill in the blanks: K-Nearest Neighbor is a a. Non-parametric , eager b. Parametric, eager c. Non-parametric, lazy d. Parametric, lazy algorithm

2. You have been given the following 2 statements. Find out which of these options is/are true in the case of k-NN. (i) In case of very large value of k , we may include points from other classes into the neighborhood. (ii) In case of too small value of k, the algorithm is very sensitive to noise. a. (i) is True and (ii) is False b. (i) is False and (ii) is True c. Both are True d. Both are False

3. State whether the statement is True/False: k-NN algorithm does more computation on test time rather than train time. a . True b. False

4. Suppose you are given the following images (1 represents the left image, 2 represents the middle and 3 represents the right). Now your task is to find out the value of k in k-NN in each of the images shown below. Here k1 is for 15, k2 is for 2nd and k3 is for 3rd figure.

a. k1 > k2> k3 b. k1 < k2> k3 c. k1 < k2 < k3 d. None of these

5. Which of the following necessitates feature reduction in machine learning? a. Irrelevant and redundant features b. Limited training data c . Limited computational resources. d. All of the above

6. Suppose, you have given the following data where x and y are the 2 input variables and Class is the dependent variable.

NPTEL Introduction To Machine Learning - IITKGP Assignment 3 Answers 2023

7. What is the optimum number of principal components in the below figure?

a. 10 b. 20 c . 30 d. 40

8. Suppose we are using dimensionality reduction as pre-processing technique, i.e, instead of using all the features, we reduce the data to k dimensions with PCA. And then use these PCA projections as our features. Which of the following statements is correct? Choose which of the options is correct? a. Higher value of ‘k’ means more regularization b. Higher value of ‘K ‘ means less regularization

9. In collaborative filtering-based recommendation, the items are recommended based on : a. Similar users b. Similar items c. Both of the above d. None of the above

10. The major limitation of collaborative f i ltering is: a. Cold start b. Overspecialization c. None of the above

11. Consider the figures below. Which figure shows the most probable PC component directions for the data points?

NPTEL Introduction To Machine Learning - IITKGP Assignment 3 Answers 2023

12. Suppose that you w i sh to reduce the number of dimensions of a given data to dimensions using PCA. Which of the following statement is correct?

a. Higher means more regularization b. Higher means less regularization c. Can’t Say

13. Suppose you are given 7 plots 1-7 (left to right) and you want to compare Pearson correlation coefficients between variables of each plot. Which of the following is true?

NPTEL Introduction To Machine Learning - IITKGP Assignment 3 Answers 2023

14. Imagine you are dealing w i th 20 class classification problem. What is the maximum number of discriminant vectors that can be produced by LDA? a. 20 b. 19 c. 21 d. 10

15. In which of the following situations collaborative filtering algorithm is appropriate? a. You manage an online bookstore and you have the book ratings from many users. For each user, you want to recommend other books he/she will like based on her previous ratings and other users’ ratings. b. You manage an online bookstore and you have the book ratings from many users. You want to predict the expected sales volume (No of books sold) as a function of average rating of a book . c. Both A and B d. None of the above

NPTEL Introduction to Machine Learning – IITKGP Assignment 3 Answers [July 2022]

Q1. Suppose, you have given the following data where x and y are the 2 input variables and Class is the dependent variable. Suppose, you want to predict the class of new data point x=1 and y=1 using euclidean distance in 3-NN. To which class the new data point belongs to? A. +Class B. – Class C. Can’t say D. None of these

2 . Imagine you are dealing with a 10 class classification problem. What is the maximum number of discriminant vectors that can be produced by LDA? A. 20 B. 14 C. 9 D. 10

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NPTEL Introduction To Machine Learning - IITKGP Assignment 3 Answers 2023

3. Fill in the blanks: K – Nearest Neighbor is a_ algorithm A. Non-parametric, eager B. Parametric, eager C. Non-parametric, lazy D. Parametric, lazy

4. Which of the following statements is True about the KNN algorithm? A. KNN algorithm does more computation on test time rather than train time. B. KNN algorithm does lesser computation on test time rather than train time. C. KNN algorithm does an equal amount of computation on test time and train time. D. None of these .

5. Which of the following necessitates feature reduction in machine learning? A. Irrelevant and redundant features B. Curse of dimensionality C. Limited computational resources. D. All of the above

6. When there is noise in data, which of the following options would improve the perfomance of the KNN algorithm? A. Increase the value of k B. Decrease the value of k C. Changing value of k will not change the effect of the noise D. None of these

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7. Find the value of the Pearson’s correlation coefficient of X and Y from the data in the following table. A. 0.47 B. 0.68 C. 1 D. 0.33

8. Which of the following is false about PCA? A. PCA is a supervised method B. It identifies the directions that data have the largest variance C. Maximum number of principal components = number of features D. All principal components are othogonal to each other

9 . In user-based collaborative filtering based recommendation, the items are recommended based on : A. Similar users B. Similar items C. Both of the above D. None of the above

10. Identify whether the following statement is true or false? “PCA can be used for projecting and visualizing data in lower dimensions . ” A. TRUE B. FALSE

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About Introduction To Machine Learning – IITKGP

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.

COURSE LAYOUT

  • Week 1:  Introduction: Basic definitions, types of learning, hypothesis space and inductive bias, evaluation, cross-validation
  • Week 2:  Linear regression, Decision trees, overfitting
  • Week 3:  Instance based learning, Feature reduction, Collaborative filtering based recommendation
  • Week 4:  Probability and Bayes learning
  • Week 5:  Logistic Regression, Support Vector Machine, Kernel function and Kernel SVM
  • Week 6:  Neural network: Perceptron, multilayer network, backpropagation, introduction to deep neural network
  • Week 7:  Computational learning theory, PAC learning model, Sample complexity, VC Dimension, Ensemble learning
  • Week 8:  Clustering: k-means, adaptive hierarchical clustering, Gaussian mixture model

CRITERIA TO GET A CERTIFICATE

Average assignment score = 25% of average of best 6 assignments out of the total 8 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 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.

ALSO READ :- NPTEL Registration Steps [July – Dec 2022] NPTEL Exam Pattern Tips & Top Tricks [2022] NPTEL Exam Result 2022 | NPTEL Swayam Result Download

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NPTEL Introduction to Machine Learning – IITM Assignment 2021

  • by QuizXp Team
  • July 28, 2021 October 20, 2021

NPTEL Introduction to Machine Learning

NPTEL INTRODUCTION TO MACHINE LEARNING – IITM course aimed at helping students enable data-driven disciplines with the increased availability of a variety of data from varied sources There has been increasing attention paid to the various methods of analytics and machine learning.

NPTEL INTRODUCTION TO MACHINE LEARNING is a MOOC course offered by IIT Madras on the NPTEL platform. This course is intend to introduce some of the basic concepts of machine learning The course is developed by Prof. Balaraman Ravindran is currently a Professor in Computer Science at IIT Madras and Mindtree Faculty Fellow.

  • Who Can Join: This is an elective course. Intended for senior UG/PG students. BE/ME/MS/PhD
  • Requirements/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.

CRITERIA TO GET A CERTIFICATE

Average assignment score = 25% of the average of the 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

Students will be eligible for CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If any of the 2 criteria are not met, the student will not get the certificate even if the Final score >= 40/100.

NPTEL Introduction to machine learning Assignment Week 12 Answers:-

Q1. In solving a classification problem, if in the learned model there is a large difference between the output of the learned model and the expected output of the learned model over various sources of variability, then we can expect _ the component of the generalisation error to be high.

Q2. Given below are some properties of different classification algorithms. In which among the following would you expect feature

Answer:- A,B,D

Q3. Which of the following measure best analyze the performance of a classifier?

Q4. As discussed in the lecture, most of the classifiers minimize the empirical risk. Which among the following is an exceptional case?

Q5. What do you expect to happen to the variance component of the generalisation error of your model as the size of the training data set increases?

Q6. What happens when your model complexity (such as interaction terms in linear regression, order of polynomial in SVM, etc.) increases?

Answer:- b,c

Q7. Suppose we want an RL agent to learn to play the game of golf. For training purposes, we make use of a golf simulator program. Assume

Q8. You want to toss a fair coin a number of times and obtain the probability of getting heads by taking a simple average. What is the

Q9. You face a particularly challenging RL problem, where the reward distribution keeps changing with time. In order to gain maximum

NPTEL Introduction to machine learning Assignment Week 11 Answers:-

Q1. During parameter estimation for a GMM model using data X, which of the following quantities are you minimizing (directly or indirectly)?

Q2. When executing the Expectation Maximization algorithm, a common problem is the sheer complexity of the number of parameters to estimate. For a typical K-Gaussian Mixture Model in an n-dimensional space, how many independent parameters are being estimated in total?

Q3. Which of the following is an assumption that reduces Gaussian Mixture Models to K-means?

Q4. Given N samples x 1, x 2,…, xN drawn independently from a Gaussian distribution with variance σ 2 and unknown mean μ . Assume that the prior distribution of the mean is also a Gaussian distribution, but with parameters mean μp and variance σ 2 p . Find the MAP estimate of the mean.

Q5. You are presented with a dataset that has hidden/missing variables that influences your data. You are asked to use Expectation Maximization algorithm to best capture the data. How would you define the E and M in Expectation Maximization?

Q6. During parameter estimation for a GMM model using data X, which of the following quantities are you minimizing (directly or indirectly)?

Q7. You are given n p-dimensional data points. The task is to learn a classifier to distinguish between k classes. You come to know that the dataset has missing values. Can you use EM algorithm to fill in the missing values ? (without making any further assumptions)

NPTEL Introduction to machine learning Assignment Week 10 Answers:-

Q1. Considering single-link and complete-link hierarchical clustering, is it possible for a point to be closer to points in other clusters than to points in its own cluster? If so, in which approach will this tend to be observed?

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Q2. Consider the following one dimensional data set: 12, 22, 2, 3, 33, 27, 5, 16, 6, 31, 20, 37, 8 and 18. Given k = 3 and initial cluster centers to be 5, 6 and 31, what are the final cluster centres obtained on applying the k -means algorithm?

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Q3. For the previous question, in how many iterations will the k-means algorithm converge?

Q4. In the lecture on the BIRCH algorithm, it is stated that using the number of points N , sum of points SUM and sum of squared points SS , we can determine the centroid and radius of the combination of any two clusters A and B. How do you determine the centroid of the combined cluster? (In terms of N,SUM and SS of both the clusters)

Q5. What assumption does the CURE clustering algorithm make with regards to the shape of the clusters?

Q6. What would be the effect of increasing MinPts in DBSCAN while retaining the same Eps parameter? (Note that more than one statement may be correct)

Q7. Visualize the dataset DS1. Which of the following algorithms will be able to recover the true clusters (first check by visual inspection and then write code to see if the result matches to what you expected).

Q8. For two independent runs of K-Mean clustering is it guaranteed to get same clustering results? Note: seed value is not preserved in independent runs.

Q9. Consider the similarity matrix given below: Which of the following shows the hierarchy of clusters created by the single link clustering algorithm.

Q10. For the similarity matrix given in the previous question, which of the following shows the hierarchy of clusters created by the complete link clustering algorithm.

NPTEL Introduction to machine learning Assignment Week 9 Answers:-

Q1. Consider the bayesian network shown below.

Two students – Manish and Trisha make the following claims:

• Manish claims P(D|{S, L, C}) = P(D|{L, C}) • Trisha claims P(D|{S, L}) = P(D|L)

Q2. Consider the Bayesian graph shown below in Figure 2.

Q3. Using the data given in the previous question, compute the probability of following assignment, P ( i =1, g =1, s =1, l =0) irrespective of the difficulty of the course? (up to 3 decimal places)

Q4. Consider the Bayesian network shown below in Figure 3

• Trisha claims P(H|{S, G, J}) = P(H|{G, J}) • Manish claims P(H|{S, C, J}) = P(H|{C, J})

Q5. Consider the Markov network shown below in Figure 4

Which of the following variables are NOT in the markov blanket of variable “4” shown in the above Figure 4 ? (multiple answers may be correct)

Answer:- d,g

Q6. In the Markov network given in Figure 4, two students make the following claims:

• Manish claims variable “1” is dependent on variable “7” given variable “2”. • Trina claims variable “2” is independent of variable “6” given variable “3”.

Q7. Four random variables are known to follow the given factorization

P ( A 1= a 1, A 2= a 2, A 3= a 3, A 4= a 4)=1 Z ψ 1( a 1, a 2) ψ 2( a 1, a 4) ψ 3( a 1, a 3) ψ 4( a 2, a 4) ψ 5( a 3, a 4)

The corresponding Markov network would be

Q8. Consider the following Markov Random Field.

Which of the following nodes will have no effect on H given the Markov Blanket of H?

Answer:- c,e,f

Q9. Select the correct pairs of (Inference Algorithm, Graphical Model) (note: more than one option may be correct)

Answer:- will update this answers soon and notify on telegram

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Q10. Here is a popular toy graphical model. It models the grades obtained by a student in a course and it’s implications. Difficulty represents the difficulty of the course and intelligence is an indicator of how intelligent the student is, SAT represents the SAT scores of the student and Letter presents the event of the student receiving a letter of recommendation from the faculty teaching the course.

Answer:- a,c,d

NPTEL Introduction to machine learning Assignment Week 8 Answers:-

Q1. In a given classification problem, there are 6 different classes. In building a classification model, we want to penalise specific errors made by the model depending upon the actual and predicted class label. For example, given a training data point belonging to class 1, if the model predicts it as class 2, then the penalty for this will be different if for the same data point, the model had predicted it as class 3. To build such a model, we need to select an appropriate

Q2. The Naive Bayes classifier makes the assumption that the ________ are independent given the ________ .

Q3. Consider the problem of learning a function X → Y , where Y is Boolean. X is an input vector ( X 1, X 2), where X 1 is categorical and takes 3 values, and X 2 is a continuous variable (normally distributed). What would be the minimum number of parameters required to define a Naive Bayes model for this function?

Q4. In boosting, the weights of data points that were miscalssified are _________ as training progresses.

Q5. In a random forest model let m << p be the number of randomly selected features that are used to identify the best split at any node of a tree. Which of the following are true? ( p is the original number of features) (Multiple options may be correct)

Q6. Consider the following data for 500 instances of home, 600 instances of office and 700 instances of factory type buildings

Q7. Consider the following graphical model, which of the following are false about the model? (multiple options may be correct)

Answer:- a,b

Q8. Consider the Bayesian network given in the previous question. Let ‘A’, ‘B’, ‘C’, ‘D’and ‘E’denote the random variables shown in the network. Which of the following can be inferred from the network structure?

NPTEL Introduction to machine learning Assignment Week 7 Answers:-

Q1. For the given confusion matrix, compute the recall

Q2. Which of the following are true? TP – True Positive, TN – True Negative, FP – False Positive, FN – False Negative

Answer:- a,c

Q3. How does bagging help in improving the classification performance?

Q4. Which method among bagging and stacking should be chosen in case of limited training data? and what is the appropriate reason for your preference?

Q5. Which of the following statements are false when comparing Committee Machines and Stacking

Q6. Which of the following measure best analyze the performance of a classifier?

Q7. For the ROC curve of True positive rate vs False positive rate, which of the following are true?

Q8. Which of the following are true about using 5-fold cross validation with a data set of size n = 100 to select the value of k in the kNN algorithm.

NPTEL Introduction to machine learning Assignment Week 6 Answers:-

Q1. Decision trees can be used for __________ .

Q2. In building a decision tree model, to control the size of the tree, we need to control the number of regions. One approach to do this would be to split tree nodes only if the resultant decrease in the sum of squares error exceeds some threshold. For the described method, which among the following are true?

Q3. In a decision tree, if we decide to swap out the usual splits (of the form xi < k or xi > k ) and instead used a linear combination of features instead, (like βTX + β 0 ), where the parameters of the hyperplane β , β 0 are also simultaneously learnt, which of the following statements would be true?

Answer:- b,d

Q4. Having built a decision tree, we are using reduced error pruning to reduce the size of the tree. We select a node to collapse. For this particular node, on the left branch, there are 3 training data points with the following outputs: 5, 7, 9.6 and for the right branch, there are four training data points with the following outputs: 8.7, 9.8, 10.5, 11. The average value of the outputs of data points denotes the response of a branch. The original responses for data points along the two branches (left right respectively) were response _ left and, response _ right and the new response after collapsing the node is response _ new . What are the values for response _ left , response _ right and response _ new (numbers in the option are given in the same order)?

Q5. Which among the following split-points for the feature 1 would give the best split according to the information gain measure?

Q6. For the same dataset, which among the following split-points for feature2 would give the best split according to the gini index measure?

Q7. In which of the following situations is it appropriate to introduce a new category ’Missing’ for missing values? (multiple options may be correct)

Answer:- a,d

NPTEL Introduction to machine learning Assignment Week 5 Answers:-

Q4. Having built a decision tree, we are using reduced error pruning to reduce the size of the tree. We select a node to collapse. For this particular node, on the left branch, there are 3 training data points with the following outputs: 5, 7, 9.6 and for the right branch,

Q1. You are given the N samples of input (x) and output (y) as shown in the figure below. What will be the most appropriate model y = f ( x )

Q2. Given N samples x 1, x 2,…, xN drawn independently from a Gaussian distribution with variance σ 2 and unknown mean μ , find the MLE of the mean.

Q3. Consider the following function.

Q4. Using the notations used in class, evaluate the value of the neural network with a 3-3-1 architecture (2-dimensional input with 1 node for the bias term in both the layers). The parameters are as follows

Answer:- WILL BE UPDATED BY MIDNIGHT AND WILL NOTIFY ON TELEGRAM , CLICK ON BELOW IMAGE FOR LINK

Q5. Which of the following statements are true:

Answer:- B,C

Q6. We have a function which takes a two-dimensional input x =( x 1, x 2) and has two parameters w =( w 1, w 2) given by f ( x , w )= σ ( σ ( x 1 w 1) w 2+ x 2) where σ ( x )=11+ e − x .We use backpropagation to estimate the right parameter values. We start by setting both the parameters to 2. Assume that we are given a training point x 2=1, x 1=0, y =3. Given this information answer the next two questions. What is the value of ∂ f ∂ w 2.

Q7. If the learning rate is 0.5, what will be the value of w 2 after one update using backpropagation algorithm?

Q8. Which of the following are true when comparing ANNs and SVMs?

Q9. Which of the following are correct?

Q10. Which of the following are false?

NPTEL Introduction to machine learning Assignment Week 4 Answers:-

Q1. Suppose we use a linear kernel SVM to build a classifier for a 2-class problem where the training data points are linearly separable. In general, will the classifier trained in this manner produce the same decision boundary as the classifier trained using the perceptron training algorithm on the same training data?

Q2. Consider the data set given below. Claim: PLA (perceptron learning algorithm) can be used to learn a classifier that achieves zero misclassification error on the training data. This claim is:

Q3. For a support vector machine model, let xi be an input instance with label yi . If yi ( β ^0+ xTiβ ^)>1 where β 0 and β ^ are the estimated parameters of the model, then

Q4. Suppose we use a linear kernel SVM to build a classifier for a 2-class problem where the training data points are linearly separable. In general, will the classifier trained in this manner be always the same as the classifier trained using the perceptron training algorithm on the same training data?

Q5. Train a linear regression model (without regularization) on the above dataset.Report the coefficients of the best fit model. Report the coefficients in the following format: β 0 β 1 β 2 β 3.

Q6. Train an l2 regularized linear regression model on the above dataset. Vary the regularization parameter from 1 to 10. As you increase the regularization parameter, absolute value of the coefficients (excluding the intercept) of the model:

Q7. Train an l 2 regularized logistic regression classifier on the modified iris dataset. We recommend using sklearn. Use only the first two features for your model. We encourage you to explore the impact of varying different hyperparameters of the model. Kindly note that the C parameter mentioned below is the inverse of the regularization parameter λ . As part of the assignment train a model with the following hyperparameters: Model: logistic regression with one-vs-rest classifier, C =1 e 4 For the above set of hyperparameters, report the best classification accuracy

Q8. Train an SVM classifier on the modified iris dataset. We recommend using sklearn. Use only the first two features for your model. 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 RBF-kernel, gamma = 0.5, one-vs-rest classifier, no-feature-normalization. Try C = 0.01, 1, 10. For the above set of hyperparameters, report the best classification accuracy along with total number of support vectors on the test data.

NPTEL Introduction to machine learning Assignment Week 3 Answers:-

Q1. Consider the case where two classes follow Gaussian distribution which are centered at (4, 7) and (−4, −1) and have identity covariance matrix. Which of the following is the separating decision boundary using LDA assuming the priors to be equal?

Q2. Consider the following data with two classes. The color indicates different class.

Q3. We discussed the use of MLE for the estimation of parameters of logistic regression model. We used which of the following assumptions to derive the likelihood function ?

Q4. Which of the following statements is true about LDA regarding outliers?

Q5. Consider the following distribution of training data:

Q6. Suppose that we have two variables, X and Y (the dependent variable). We wish to find the relation between them. An expert tells us that relation between the two has the form Y = m log( X )+ c . Available to us are samples of the variables X and Y . Is it possible to apply linear regression to this data to estimate the values of m and c ?

Q7. In a binary classification scenario where x is the independent variable and y is the dependent variable, logistic regression assumes that the conditional distribution y | x follows a

Q8. Assuming that you apply LDA to this data, what is the estimated covariance matrix?

Answer:- F (THIS MIGHT BE WRONG PLEASE CHECK AT YOUR LEVEL)

Q9. Given the following 3D input data, identify the principal component. (Steps: center the data, calculate the sample covariance matrix, calculate the eigenvectors and eigenvalues, identify the principal component)

Answer:- B (THIS MIGHT BE WRONG PLEASE CHECK AT YOUR LEVEL)

Q10. For the data given in the previous question, find the transformed input along the first two principal components.

NPTEL Introduction to machine learning Assignment Week 2 Answers:-

Q1. Given a training dataset, the following visualization shows the fit of three different models (in blue line). Assume that the test data and training data come from the same distribution. What can you conclude from the following visualizations? Multiple options can be correct.

Answer:- A,C,D

Q2. Suppose you have fitted a complex regression model on a dataset. Now, you are using Ridge regression with tuning parameter lambda to reduce its complexity. Choose the option below which describes relationship of bias and variance with lambda.

Q3. Given a training data set of 10,000 instances, with each input instance having 17 dimensions and each output instance having 2 dimensions, the dimensions of the design matrix used in applying linear regression to this data is

Q4. Suppose we want to add a regularizer to the linear regression loss function, to control the magnitudes of the weights β . We have a choice between Ω1( β )=∑ i =1 p | β | and Ω2( β )=∑ i =1 pβ 2. Which one is more likely to result in sparse weights?

Q5. Consider forward selection, backward selection and best subset selection with respect to the same data set. Which of the following is true?

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Q6. In the formulation of the method, we observe that in iteration k , we regress the entire dataset on z 0, z 1,… zk −1 . It seems like a waste of computation to recompute the coefficients for z 0 a total of p times, z 1 a total of p −1 times and so on. Can we re-use the coefficients computed in iteration j for iteration j +1 for zj −1 ?

Q7. Consider the following five training examples We want to learn a function f ( x ) of the form f ( x )= ax + b which is parameterised by ( a , b ). Using squared error as the loss function, which of the following parameters would you use to model this function to get a solution with the minimum loss.

Q8. Here is a data set of words in two languages.

NPTEL Introduction to machine learning Assignment Week 1 Answers:-

Q1 . Which of the following is a supervised learning problem?

Answer:- B,C,D

Q2 – Which of the following is not a classification problem?

Answer:- A,C

Q3 – Which of the following is a regression task? (multiple options may be correct)

Note:- WE NEVER PROMOTE COPYING AND We do not claim 100% surety of answers, these answers are based on our sole knowledge, and by posting these answers we are just trying to help students to reference, so we urge do you assignment on your own.

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Q4 – Which of the following is an unsupervised task?

Answer:- C,D

Q5 – Which of the following is a categorical feature?

Answer:- D,F

Q6 – Let X and Y be a uniformly distributed random variable over the interval [0, 4] and [0, 6] respectively. If X and Y are independent events, then compute the probability, P(max( X , Y )>3)

Answer:- F – 5/8

NOTE:- IF THERE IS ANY CHANGE IN ANSWERS OF NPTEL Introduction to Machine Learning WILL UPDATE BEFORE LAST DATE AND NOTIFY ON TELEGRAM OR WHATSAPP. SO KINDLY JOIN US, CLICK ON BELOW IMAGE AND JOIN US.

Q7 – Let the trace and determinant of a matrix A [ acbd ] be 6 and 16 respectively. The eigenvalues of A are.

Answer:-E -3+ ı 7–√,3− ı 7–√where ı =−1

Q8 – What happens when your model complexity increases? (multiple options may be correct)

Q9 – A new phone, E-Corp X1 has been announced and it is what you’ve been waiting for, all along. You decide to read the reviews before buying it. From past experiences, you’ve figured out that good reviews mean that the product is good 90% of the time and bad reviews mean that it is bad 70% of the time. Upon glancing through the reviews section, you find out that the X1 has been reviewed 1269 times and only 172 of them were bad reviews. What is the probability that, if you order the X1, it is a bad phone?

Answer:- G – 0.181

Q10 – Which of the following are false about bias and variance of overfitted and underfitted models? (multiple options may be correct)

NPTEL Introduction to machine learning Assignment Week 0 Answers:-

Q1. There are n bins of which the k -th bin contains k −1 blue balls and n − k red balls. You pick a bin at random and remove two balls at random without replacement. Find the probability that:

Answer:- C – 1/2,2/3

Q2. A medical company touts its new test for a certain genetic disorder. The false negative rate is small: if you have the disorder, the probability that the test returns a positive result is 0.999. The false positive rate is also small: if you do not have the disorder, the probability that the test returns a positive result is only 0.005. Assume that 2% of the population has the disorder. If a person chosen uniformly from the population is tested and the result comes back positive, what is the probability that the person has the disorder?

Answer:- A – 0.803

Q3. In an experiment, n coins are tossed, with each one showing up heads with probability p independently of the others. Each of the coins which shows up heads is then tossed again. What is the probability of observing 5 heads in the second round of tosses, if we toss 15 coins in the first round and p = 0.4?

Answer:- B – 0.055

Q4. Consider two random variables X and Y having joint density function f ( x , y )=2 e − x − y ,< x < y <∞. Are X and Y independent? Find the covariance of X and Y .

Answer:- A – Yes, 1/4

Q5. An airline knows that 5 percent of the people making reservations on a certain flight will not show up. Consequently, their policy is to sell 52 tickets for a flight that can hold only 50 passengers. What is the probability that there will be a seat available for every passenger who shows up?

Answer:- D – 0.7405

Q6. Let X have mass function  f ( x )={{ x ( x +1)}−10if x =1,2,…,otherwise,

Answer:- B – α <1

Q7. Is the following a distribution function?

Answer:- A – Yes, x −2 e −1/ x , x >0

Q8. Can the value of a probability density function be greater than one? What about the cumu- lative distribution function?

Answer:- B – PDF: yes, CDF: no

Q9. You are given a biased coin with probability of seeing a head is p = 0.6 and probability of seeing a tail is q = 0.4. Suppose you toss the coin 10 times, what is the probability of you getting the head at most 2 times? Also, what is the probability of you getting the head for the first time on your fourth attempt?

Answer:- A – 0.012, 0.038

Q10. Given a bag containing 6 red balls, 4 blue balls and 7 green balls, what is the probability that in 5 trials, at least 3 red balls are drawn from the bag?

Answer:- A – 0.24

Q11. In the experiment from the previous question, what is the probability of picking a red ball for the first time on the fourth attempt?

Answer:- C – 0.096

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introduction to machine learning nptel assignment 3 answers

Introduction to Machine Learning (IITKGP) :Thank you for learning with NPTEL!

Dear Learner, Thank you for taking the course with NPTEL!! Hope you enjoyed the journey with us. The results for this course with exam on Sep 29th have been published and we are closing this course now.  You will still have access to the contents and assignments of this course, if you click on the course name from the "Mycourses" tab on  swayam.gov.in . The discussion forum is being closed though and you cannot ask questions here. For any further queries please write to  [email protected] . We are happy to announce that our next set of courses to be offered in Jan 2020 - will be open for enrollment from 18 November 2019 onwards. Please check the Tentative course list for Jan 2020 in the below link: http://bit.ly/JAN2020-NPTEL - Team NPTEL

LIVE INTERACTIVE SESSION WITH NPTEL COORDINATORS - OCT 23 (WEDNESDAY) 4:30 pm

Dear candidates, NPTEL Coordinators would like to address all the candidates about this on-going semester, course run, exams, results, etc.  This LIVE interactive session is meant to clarify any queries you may have. Please do not ask technical questions about the course content, Assignment, etc. as we will not be able to answer these. Kindly fill this form with your queries which will be discussed during the LIVE session. You may ask queries via the chat window during the session. DAY: OCTOBER 23, 2019 (Wednesday) TIME: 4:30pm Link to submit queries/suggestions :  https:// forms.gle/AD5tLgGK2oSJxFi98 YouTube Link for Live Session :  https://youtu.be/ 1xKn1vfnJFk -NPTEL Team

Introduction to Machine Learning (IITKGP) : Results for Sep 29 exams have been published

Dear Learner,  The results for Sep 29,2019 exams have been published.  You will be informed via mail and SMS about the release of exam results and e-certificates.  How to find out if results have been published?  To check the publishing status of exam scores and e-certificates, click on this link:  http://bit.ly/septemberresults  How to check exam results & e-certificates:  To check the results & see your e-certificate, go to  https://results.nptel.ac.in/   Login with the course enrolled e-mail id.  Clicking on the course name will redirect to the score board.  Click on Exam Scores - Both the Assignment scores and Exam score will be displayed.  Final score is the certification score.  Calculation Logic for each course is provided in the same page.  Reporting of errors  For each course, reporting of issues comes with its own deadline (date and time)  So check the exam score/e-certificate as soon as you receive our notification.  For any queries, please use this  FAQ   http://bit.ly/scoringfaq   If you want to lodge any issues about scores/e-certificates, you may do that in  http://bit.ly/scoringerror   If you are unable to see the scores/e-certificates, please write to  [email protected]  - NPTEL TEAM

Feedback For Introduction to Machine Learning (IITKGP)

Best of luck for your exam.

Dear Learners,

introduction to machine learning nptel assignment 3 answers

Best of luck for your examination!

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Introduction to Machine Learning - Solution for Week 8 assignment

Detailed Solution for assignment 8 is available now in the Course Outline section. Please go through the solution and in case of any doubt post your queries in the forum.

Introduction to Machine Learning - Week 1 to 8 Feedback Form

Thank you for enrolling to this NPTEL course and we hope you have gone through the contents for this week and also attempted the assignment.

We value your feedback and wish to know how you found the videos and the questions asked - whether they were easy, difficult, as per your expectations, etc

We shall use this to make the course better and we can also know from the feedback which concepts need more explanation, etc.

Please do spare some time to give your feedback - comprises just 5 questions - should not take more than a minute, but makes a lot of difference for us as we know what the learners feel.

Here is the link to the form: https://docs.google.com/forms/d/1Z1y5qWDIyk7xFvS630Das1QmOzq2LdyFCD1kY6j0Ii4/viewform

Introduction to Machine Learning - Solution for Week 7 assignment

Detailed Solution for assignment 7 is available now in the Course Outline section. Please go through the solution and in case of any doubt post your queries in the forum.

Introduction to Machine Learning : Live session feedback

Dear Learner, We would also like to hear from you after the session. Request you to share your thoughts in the feedback form:  https://docs.google.com/forms/d/e/1FAIpQLSf3Oc1IRpDgTI3rzd-KDMixOOQqcVZ-bsTXr5BfhCxqEW41yA/viewform Learners are encouraged to visit  bit.ly/NPTELLIVE  for updates on the live sessions. -NPTEL team

Introduction to Machine Learning : Video recording on Interactive session

Introduction to machine learning - solution for week 6 assignment.

Detailed Solution for assignment 6 is available now in the Course Outline section. Please go through the solution and in case of any doubt post your queries in the forum.

Introduction to Machine Learning - Week 8 is live now!!

Dear Students

The lecture videos for Week 8 have been uploaded for the course Introduction to Machine Learning . The lectures can be accessed using the following https://onlinecourses.nptel.ac.in/noc19_cs52/unit?unit=61&lesson=62

The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already).

Assignment for   Week 8 is also uploaded and can be accessed from the following link: https://onlinecourses.nptel.ac.in/noc19_cs52/unit?unit=61&assessment=88

The assignment has to be submitted on or before   2019-09-25, 23:59 IST.

As we have done so far, please use the discussion forums if you have any questions on this module.

--NPTEL Team

Introduction to Machine Learning - Solution for Week 5 assignment

Detailed Solution for assignment 5 is available now in the Course Outline section. Please go through the solution and in case of any doubt post your queries in the forum.

Introduction to Machine Learning - Week 7 is live now!!

The lecture videos for Week 7 have been uploaded for the course Introduction to Machine Learning . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc19_cs52/unit?unit=53&lesson=54

Assignment for   Week 7 is also uploaded and can be accessed from the following link:   https://onlinecourses.nptel.ac.in/noc19_cs52/unit?unit=53&assessment=86

The assignment has to be submitted on or before   2019-09-18, 23:59 IST.

Introduction to Machine Learning- Week 6 is live now!!

The lecture videos for Week 6 have been uploaded for the course Introduction to Machine Learning . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc19_cs52/unit?unit=45&lesson=46

Assignment for   Week 6 is also uploaded and can be accessed from the following link: https://onlinecourses.nptel.ac.in/noc19_cs52/unit?unit=45&assessment=84

The assignment has to be submitted on or before   2019-09-11, 23:59 IST.

Introduction to Machine Learning (IITKGP): Exam Type and Certificate Format

Dear Candidate, Type of exam: Computer based exam  You will have to appear at the allotted exam center and produce your Hall ticket and Government Photo Identification Card (Example: Driving License, Passport, PAN card, Voter ID, Aadhaar-ID with your Name, date of birth, photograph and signature) for verification and take the exam in person.  You can find the final allotted exam center details in the hall ticket. The questions will be on the computer and the answers will have to be entered on the computer; type of questions may include multiple choice questions, fill in the blanks, essay-type answers, etc. The hall ticket will be available for download tentatively around  Sep 15 - 25, 2019 . We will notify the same through email and SMS. On-Screen Calculator Demo Link: Kindly use the below link to get an idea of how the On-screen calculator will work during the exam. https://tcsion.com/OnlineAssessment/ScientificCalculator/Calculator.html NOTE: Physical calculators are not allowed inside the exam hall.  FINAL CERTIFICATE: The final score = 25% assignment score + 75% final certification exam score. YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF 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. The final score will determine if you will/will not receive a certificate. 1. Final score  < 40%: NO certificate. 2. Final score between  40% -59%:  Certificate of type  "Successfully completing the course". 3. Final score between  60% -74%:  Certificate with tag  "Elite"  printed at the top. 4. Final score between  75% -89%:  Certificate with tag  "Elite" tag and "Silver medal"  printed at the top. 5. Final score of  90% and above : Certificate with  "Elite"  tag and the  "gold medal"  printed on it. -NPTEL Admin

Introduction to Machine Learning - Solution for Week 4 assignment

Detailed Solution for assignment 4 is available now in the Course Outline section. Please go through the solution and in case of any doubt post your queries in the forum.

Reg: Assignment 2 question no 7

Dear Students, There is change in answer in assignment 2 question no-7. The re-evaluation will be done shortly. The updated score will be displayed in the Progress tab after re-evaluation. Sorry for the inconvenience caused. -NPTEL Team

Introduction to Machine Learning - Week 5 is live now!!

The lecture videos for Week 5 have been uploaded for the course Introduction to Machine Learning . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc19_cs52/unit?unit=36&lesson=37

Assignment for   Week 5 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc19_cs52/unit?unit=36&assessment=80

The assignment has to be submitted on or before   2019-09-04, 23:59 IST.

Introduction to Machine Learning - Solution for Week 2 & 3 assignment

Detailed Solution for assignment 2 & 3 are available now in the Course Outline section. Please go through the solution and in case of any doubt post your queries in the forum.

Reg: Assignment 3 question no 9

Dear Students, There is change in answer in assignment 3 question no 9. The re-evaluation will be done shortly. The updated score will be displayed in Progress tab after re-evaluation. Sorry for the inconvenience caused. -NPTEL Team

Reg: Assignment 2 question no 5

Dear Students, There is change in answer in assignment 2 question no 5. The re-evaluation will be done shortly. The updated score will be displayed in  Progress  tab after re-evaluation. Sorry for the inconvenience caused. -NPTEL Team

Announcement regarding ipynb for Tutorial 1

The first tutorial ipynb has been fixed  .  Thanks to Anirban Santara. Please check the same in the link. For all of you having problems installing dependencies, go through this link  . This is a new initiative by Google to open public github repos in their colab.

Introduction to Machine Learning - Solution for Week 1 assignment

Detailed Solution for assignment 1 is available now in the Course Outline section. Please go through the solution and in case of any doubt post your queries in the forum.

Introduction to Machine Learning - Week 4 is live now!!

The lecture videos for Week 4 have been uploaded for the course Introduction to Machine Learning . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc19_cs52/unit?unit=29&lesson=30

Assignment for   Week 4 is also uploaded and can be accessed from the following link: https://onlinecourses.nptel.ac.in/noc19_cs52/unit?unit=29&assessment=77  

The assignment has to be submitted on or before   Wednesday, 2019-08-28, 23:59 IST .

Introduction to Machine Learning (IITKGP) : Due date for Assignment 2 Extended!

Dear Learners Based on requests received, we are extending the deadline of the assignment 2. Deadline for Assignment 2 has been extended till August 21, 2019 - 23:59 IST Assignment 1 will close on 14 August 2019  - 23:59 IST , as per the original deadline.  - NPTEL Team

Introduction to Machine Learning- Week 2 Feedback Form

Here is the link to the form: http://nptel.ac.in/noc/nocprofile/super_admin/weekly_feedback/form_login.php

Introduction to Machine Learning - Week 3 is live now!!

The lecture videos for Week 3 have been uploaded for the course Introduction to Machine Learning . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc19_cs52/unit?unit=21&lesson=22

Assignment for   Week 3 is also uploaded and can be accessed from the following link: https://onlinecourses.nptel.ac.in/noc19_cs52/unit?unit=21&assessment=74

The assignment has to be submitted on or before Wednesday, 2019-08-21, 23:59 IST .

Introduction to Machine Learning (IITKGP) - Week 2 is live now!!

The lecture videos for Week 2 have been uploaded for the course Introduction to Machine Learning (IITKGP) . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc19_cs52/unit?unit=13&lesson=14

Assignment for  Week 2 is also uploaded and can be accessed from the following link: https://onlinecourses.nptel.ac.in/noc19_cs52/unit?unit=13&assessment=73

The assignment has to be submitted on or before : Wednesday, 2019-08-14, 23:59 IST .

Introduction to Machine Learning (IITKGP): Steps to access the assignment through Mobile App

Dear Learners  Assignments have been released for all courses for week 1. If you are using the mobile app, please ensure you see the assignment link. If you don't, please do the following steps: Step 1: Go to setting on your android phone Step 2: Go to the “Apps” in the settings Step3: You will get a list of apps. Click on Swayam app Step 4: You will see app info. Go to the storage in the app info Step 5: Click on clear data and clear cache button. Step 6: Login again to the SWAYAM app using the credentials used to enroll in the course. Please note that if the OS version in your Android phone is 6, the content may not show up properly. Kindly refer to the web version to ensure you see all the contents of the week and then proceed to use the app.  -NPTEL Team

Introduction to Machine Learning - Week 1 assignment is live now!!

The assignment for Week 1 for the course Introduction to Machine Learning is made available early for viewing to get an idea about the assignments but the actual start date of the course remains unchanged.

Assignment 1 can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc19_cs52/unit?unit=6&assessment=70

The other Assignment 1 is accessible from the navigation bar to the left under Week 1. Please remember to login into the website to view contents (if you aren't logged in already).

The assignment has to be submitted on or before Wednesday, 2019-08-14, 23:59 IST.

Please use the discussion forums if you have any questions on this module.

Happy Learning!

-NPTEL Team

Introduction to Machine Learning - Week 1 videos are live now!!

The videos for Week 1 for the course Introduction to Machine Learning is made available early for viewing to get an idea about the content but the actual start date remains unchanged.

The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc19_cs52/unit?unit=6&lesson=7

Introduction to Machine Learning - Assignment-0-RELEASED

Dear Learners, We welcome you all to this course. The assignment 0 for the course  Introduction to Machine Learning  has been released. This assignment is based on prerequisite of the course. Kindly note that marks obtained in this assignment will not be considered for the final assessment. You can find the assignment under Week 0 unit on the left-hand side of your screen You can submit the assignment multiple times before the due date.  The due date of the assignment is  Aug 31, 2019, 23:59 hrs .  All the best !!            --NPTEL Team

REMINDER 2: Introduction to Machine Learning: REGISTER TODAY - CERTIFICATION EXAM FORM IS NOW OPEN!

Dear Learner,  Here is the much-awaited announcement on  registering  for the  Sep 2019 NPTEL online  certification exam.   1.    The  registration  for the certification exam is  open  only to those learners who have enrolled in the course. 2.    If you want to  register  for the exam for this course,  login here using the same email id which you had used to enroll to the course in Swayam portal .  3.    Till the start date of the course,  every Monday and Thursday at 5:00 PM ,  we will sync the enrollment data on the exam form. If you enroll in between,  please wait till the nearest Tuesday/Friday 10:00 AM to  register  for the exam . Once the enrollment is closed, the enrollment data will be completely loaded and you can  register  any time. 4.     Date of exam: Sep 29, 2019     •         Certification exam  registration  URL is:  http://nptelonlinecourses. iitm.ac.in/ •         Choose an exam session:  Forenoon: 9.00 AM -12.00 PM; Afternoon: 2.00PM - 5.00 PM •         Choose from the Cities where exam will be conducted:   list of exam cities 5.     Exam fees: •         If you  register  for exam and pay before  Aug 19, 10:00 AM, Exam fees will be Rs. 1000/- per exam. •         If you  register  for exam before  Aug 19 , 10:00 AM and have not paid or if you have  registered  between  Aug 19 , 10:00 AM &  Aug 23 , 5:00 PM, Exam fees will be Rs. 1500/- per exam   6.     50% fee waiver for following categories: •         Students belonging to the SC/ST category: please select Yes for the SC/ST option and upload the correct Community certificate •         Students belonging to the PwD category with more than 40% disability: please select Yes for the option and upload the relevant Disability certificate.   7.     Last date for exam  registration :   Aug 23, 2019 5:00 PM (Friday).   8.     Mode of payment:   Online payment - debit card/credit card/net banking or via SPOC of college (refer to  guidelines  for “Pay via SPOC” instructions)   9.     HALL TICKET : The hall ticket will be available for download tentatively by  2 weeks prior to exam date .  We will confirm the same through an announcement once it is published.   10.  Final score on certificate: 25% of assignment score + 75% of certification exam score. Award of certificate:  No hard copy of certificate will be printed. The soft copy of certificate will be awarded only to those candidates who  register  for the exam, attend the certification examination and whose  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.     Please check the Announcements section of your course for further details on this.    11.  FOR CANDIDATES WHO WOULD LIKE TO WRITE MORE THAN 1 COURSE EXAM: - you can add or delete courses and pay separately – till the date when the exam form closes. Same day of exam – you can write exams for 2 courses in the 2 sessions. Same exam center will be allocated for both the sessions.   12.  Data changes   Following are the data that can be changed once the exam form is submitted (Last date for data changes:  23 Aug 2019, 5:00 PM ): •        Data that can be changed in form by candidates themselves: Name, DOB, Address, College name, Photo, Signature, Exam city •        Data that can be changed by Email request to us : Course selected, exam shift. For changes in these parameters, you may send email to  [email protected]   giving your Application number, email id and name.  •        No changes will be entertained in any details after  23 Aug 2019, 5:00 PM.   13.  LAST DATE FOR CANCELLING EXAMS and getting a refund:  23 Aug 2019, 5:00 PM 14. Click here to view Timeline and Guideline :  Guideline Thanks & Regards, NPTEL TEAM

Welcome to SWAYAM-NPTEL Online Course - Introduction to Machine Learning

  • Every week, about 2.5 to 4 hours of videos containing content by the Course instructor will be released along with an assignment based on this.  Please watch the lectures, follow the course regularly and submit all assessments and assignments before the due date. Your regular participation is vital for learning and doing well in the course. This will be done week on week through the duration of the course.
  • Please do the assignments yourself and even if you take help, kindly try to learn from it. These assignment will help you prepare for the final exams. Plagiarism and violating the Honor code will be taken very seriously if detected during the submission of assignments. 
  • The announcement group - will only have messages from course instructors and teaching assistants - regarding the lessons, assignments, exam registration, hall tickets etc.    
  • The discussion forum (Ask a question tab on the portal) - is for everyone to ask questions and interact.Anyone who knows the answers can reply to anyone's post and the course instructor/TA will also respond to your queries. Please make maximum use of this feature as this will help you learn much better.
  • If you have any questions regarding the exam, registration, hall tickets, results, queries related to the technical content in the lectures, any doubts in the assignments, etc can be posted in the forum section
  • The course is free to enroll and learn from. But if you want a certificate, you have to register and write the proctored exam conducted by us in person at any of the designated exam centres.
  • The exam is optional for a fee of  Rs 1000/- (Rupees one thousand only).
  • Date and Time of Exams: 29th  September 2019  ,Morning session 9am to 12 noon; Afternoon Session 2pm to 5pm.
  • Registration url:  Announcements will be made when the registration form is open for registrations.
  • The online registration form has to be filled and the certification exam fee needs to be paid. More details will be made available when the exam registration form is published. If there are any changes, it will be mentioned then.
  • Please check the form for more details on the cities where the exams will be held, the conditions you agree to when you fill the form etc.
  • Average assignment score = 25% of average of best 6 assignments out of the total 8 assignments given in the course. 
  • Exam score = 75% of the proctored certification exam score out of 100
  • Final score = Average assignment score + Exam score

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introduction to machine learning nptel assignment 3 answers

In association with

introduction to machine learning nptel assignment 3 answers

  • Computer Science and Engineering
  • NOC:Introduction to Machine Learning(Course sponsored by Aricent) (Video) 
  • Co-ordinated by : IIT Madras
  • Available from : 2016-01-19
  • Intro Video
  • A brief introduction to machine learning
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
  • Probability Basics - 1
  • Probability Basics - 2
  • Linear Algebra - 1
  • Linear Algebra - 2
  • Statistical Decision Theory - Regression
  • Statistical Decision Theory - Classification
  • Bias-Variance
  • Linear Regression
  • Multivariate Regression
  • Subset Selection 1
  • Subset Selection 2
  • Shrinkage Methods
  • Principal Components Regression
  • Partial Least Squares
  • Linear Classification
  • Logistic Regression
  • Linear Discriminant Analysis 1
  • Linear Discriminant Analysis 2
  • Linear Discriminant Analysis 3
  • Weka Tutorial
  • Optimization
  • Perceptron Learning
  • SVM - Formulation
  • SVM - Interpretation & Analysis
  • SVMs for Linearly Non Separable Data
  • SVM Kernels
  • SVM - Hinge Loss Formulation
  • Early Models
  • Backpropogation I
  • Backpropogation II
  • Initialization, Training & Validation
  • Maximum Likelihood Estimate
  • Priors & MAP Estimate
  • Bayesian Parameter Estimation
  • Introduction
  • Regression Trees
  • Stopping Criteria & Pruning
  • Loss Functions for Classification
  • Categorical Attributes
  • Multiway Splits
  • Missing Values, Imputation & Surrogate Splits
  • Instability, Smoothness & Repeated Subtrees
  • Evaluation Measures I
  • Bootstrapping & Cross Validation
  • 2 Class Evaluation Measures
  • The ROC Curve
  • Minimum Description Length & Exploratory Analysis
  • Introduction to Hypothesis Testing
  • Basic Concepts
  • Sampling Distributions & the Z Test
  • Student\'s t-test
  • The Two Sample & Paired Sample t-tests
  • Confidence Intervals
  • Bagging, Committee Machines & Stacking
  • Gradient Boosting
  • Random Forest
  • Naive Bayes
  • Bayesian Networks
  • Undirected Graphical Models - Introduction
  • Undirected Graphical Models - Potential Functions
  • Hidden Markov Models
  • Variable Elimination
  • Belief Propagation
  • Partitional Clustering
  • Hierarchical Clustering
  • Threshold Graphs
  • The BIRCH Algorithm
  • The CURE Algorithm
  • Density Based Clustering
  • Gaussian Mixture Models
  • Expectation Maximization
  • Expectation Maximization Continued
  • Spectral Clustering
  • Learning Theory
  • Frequent Itemset Mining
  • The Apriori Property
  • Introduction to Reinforcement Learning
  • RL Framework and TD Learning
  • Solution Methods & Applications
  • Multi-class Classification
  • Watch on YouTube
  • Assignments
  • Download Videos
  • Transcripts
  • Handouts (1)

NPTEL Introduction to Machine Learning Week 8 Assignment Answers 2024

1. Consider the Bayesian network given below. Which of the following statement(s) is/are correct?

NPTEL Introduction to Machine Learning Week 8 Assignment Answers 2024

  • B is independent of F, given D.
  • A is independent of E, given C.
  • E and F are not independent, given D.
  • A and B are not independent, given D.

NPTEL Introduction to Machine Learning Week 8 Assignment Answers 2024

3. A decision tree classifier learned from a fixed training set achieves 100% accuracy. Which of the following models trained using the same training set will also achieve 100% accuracy? (Assume P(x i |c) as Gaussians) I Logistic Regressor. II A polynomial of degree one kernel SVM. III A linear discriminant function. IV Naive Bayes classifier.

  • None of the above.

4. Which of the following points would Bayesians and frequentists disagree on?

  • The use of a non-Gaussian noise model in probabilistic regression.
  • The use of probabilistic modelling for regression.
  • The use of prior distributions on the parameters in a probabilistic model.
  • The use of class priors in Gaussian Discriminant Analysis.
  • The idea of assuming a probability distribution over models

5. Consider the following data for 500 instances of home, 600 instances of office and 700 instances of factory type buildings

NPTEL Introduction to Machine Learning Week 8 Assignment Answers 2024

Suppose a building has a balcony and power-backup but is not multi-storied. According to the Naive Bayes algorithm, it is of type

6. In AdaBoost, we re-weight points giving points misclassified in previous iterations more weight. Suppose we introduced a limit or cap on the weight that any point can take (for example, say we introduce a restriction that prevents any point’s weight from exceeding a value of 10). Which among the following would be an effect of such a modification? (Multiple options may be correct)

  • We may observe the performance of the classifier reduce as the number of stages increase
  • It makes the final classifier robust to outliers
  • It may result in lower overall performance
  • It will make the problem computationally infeasible

7. While using Random Forests, if the input data is such that it contains a large number (> 80%) of irrelevant features (the target variable is independent of the these features), which of the following statements are TRUE?

  • Random Forests have reduced performance as the fraction of irrelevant features increases.
  • Random forests have increased performance as the fraction of irrelevant features increases.
  • The fraction of irrelevant features doesn’t impact the performance of random forest.

8. Suppose you have a 6 class classification problem with one input variable. You decide to use logistic regression to build a predictive model. What is the minimum number of (β0,β) parameter pairs that need to be estimated?

IMAGES

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VIDEO

  1. Introduction To Machine Learning || Week 1 Assignment || NPTEL 2023

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  4. Introduction To Machine Learning Week 8 Assignment 8 Solution || NPTEL || Swayam || July to Oct 2023

  5. NPTEL: Introduction to Machine Learning Week 6 Quiz Answers

  6. Assignment -3 || Week -3 || Introduction To Machine Learning- IITKGP || NPTEL 2022 ||

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