introduction to machine learning nptel assignment answers 2022

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NPTEL Introduction to Machine Learning | Week 1 Assignment Answer

Welcome to this comprehensive guide where I present the answers to the Week 1 assessment of NPTEL's Introduction to Machine Learning course. This course is designed to lay the groundwork for understanding the core concepts and methodologies that drive modern machine learning applications.

Machine learning, a cornerstone of artificial intelligence, empowers computers to learn from data and make informed decisions without explicit programming. Week 1 of this course focuses on foundational principles such as data preprocessing, exploratory data analysis (EDA), and statistical techniques essential for machine learning practitioners.

Throughout this post, I will meticulously address each multiple-choice question (MCQ) from the Week 1 assignment. The answers provided are meticulously crafted based on the rigorous study of course materials, ensuring accuracy and clarity in understanding fundamental ML concepts.

Whether you are a student aspiring to enter the field of data science, a professional seeking to enhance your skills, or an enthusiast curious about the intricacies of machine learning, this blog aims to serve as a valuable resource. By elucidating the rationale behind each answer, I aim to facilitate a deeper comprehension of machine learning fundamentals and their practical applications.

Here are Introduction to Machine Learning Week 1 Assignment Answers

Q1. Which of the following is/are unsupervised learning problem(s)?

• Grouping documents into different categories based on their topics
• Forecasting the hourly temperature in a city based on historical temperature patterns
• Identifying close-knit communities of people in a social network
• Training an autonomous agent to drive a vehicle
• Identifying different species of animals from images

Answer: A, C Grouping documents into different categories based on their topics Identifying close-knit communities of people in a social network

Q2. Which of the following statement(s) about Reinforcement Learning (RL) is/are true?

• While learning a policy, the goal is to maximize the long-term reward.
• During training, the agent is explicitly provided the most optimal action to be taken in each state.
• The state of the environment changes based on the action taken by the agent.
• RL is used for building agents to play chess.
• RL is used for predicting the prices of apartments from their features.

Answer: A, C, D While learning a policy, the goal is to maximize the long-term reward. The state of the environment changes based on the action taken by the agent. RL is used for building agents to play chess.

Q3. Which of the following is/are classification tasks(s)?

• Predicting whether an email is spam or not spam
• Predicting the number of COVID cases over a given period
• Predicting the score of a cricket team
• Identifying the language of a text document

Answer: A, D Predicting whether an email is spam or not spam Identifying the language of a text document

Q4. Which of the following is/are regression task(s)?

• Predicting whether or not a customer will repay a loan based on their credit history
• Forecasting the amount of rainfall in a given place
• Identifying the types of crops from aerial images of farms
• Predicting the future price of a stock

Answer: B, D Forecasting the amount of rainfall in a given place Predicting the future price of a stock

Q5. Consider the following dataset. Fit a linear regression model of the form y=β0+β1×1+β2×2 using the mean-squared error loss. Using this model, the predicted value of y at the point (x1,x2)=(0.5,−1.0) is:

• −0.651
• −0.737
• −0.872

Answer: −0.737

Q6. Consider the following dataset. Using a k-nearest neighbour (k-NN) regression model with k=3, predict the value of y at (x1,x2)=(0.5,−1.0). Use the Euclidean distance to find the nearest neighbours.

• −1.762
• −2.061
• −1.930
• −1.529

Answer: −1.930

Q7. Consider the following statements regarding linear regression and k-NN regression models. Select the true statements.

• A linear regressor requires the training data points during inference.
• A k-NN regressor requires the training data points during inference.
• A k-NN regressor with a higher value of k is less prone to overfitting.
• A linear regressor partitions the input space into multiple regions such that the prediction over a given region is constant.

Answer: B, C A k-NN regressor requires the training data points during inference. A k-NN regressor with a higher value of k is less prone to overfitting.

Q8. Consider a binary classification problem where we are given certain measurements from a blood test and need to predict whether the patient does not have a particular disease (class 0) or has the disease (class 1). In this problem, false negatives (incorrectly predicting that the patient is healthy) have more serious consequences as compared to false positives (incorrectly predicting that the patient has the disease). Which of the following is an appropriate cost matrix for this classification problem? The row denotes the true class and the column denotes the predicted class.

• [0 0 100 0]
• [0 1 100 0]
• [0 100 1 0]
• [0 100 0 0]

Answer: D. [0 1 100 0]

Q9. Consider the following dataset with three classes: 0, 1 and 2. x1 and x2 are the independent variables whereas y is the class label. Using a k-NN classifier with k = 3, predict the class label at the point (x1,x2)=(0.7,−0.8). Use the Euclidean distance to find the nearest neighbours.

• Cannot be predicted

Q10. Suppose that we train two kinds of regression models corresponding to the following equations. (i) y=β0+β1×1+β2×2 (ii) y=β0+β1×1+β2×2+β3x1x2 Which of the following statement(s) is/are correct?

• On a given training dataset, the mean-squared error of (i) is always greater than or equal to that of (ii).
• (i) is likely to have a higher variance than (ii).
• (ii) is likely to have a higher variance than (i).
• If (ii) overfits the data, then (i) will definitely overfit.
• If (ii) underfits the data, then (i) will definitely underfit.

Answer: A, C, E On a given training dataset, the mean-squared error of (i) is always greater than or equal to that of (ii). (ii) is likely to have a higher variance than (i). If (ii) underfits the data, then (i) will definitely underfit.

Introduction to Machine Learning - July-Dec 2023 Assignment Answers

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

• Grouping related documents from an unannotated corpus.
• Predicting credit approval based on historical data.
• Predicting if a new image has cat or dog based on the historical data of other images of cats and dogs, where you are supplied the information about which image is cat or dog.
• Fingerprint recognition of a particular person used in biometric attendance from the fingerprint data of various other people and that particular person.

Answer: B, C, D

Q2. Which of the following are classification problems?

• Predict the runs a cricketer will score in a particular match.
• Predict which team will win a tournament.
• Predict whether it will rain today.
• Predict your mood tomorrow.

Q3. Which of the following is a regression task?

• Predicting the monthly sales of a cloth store in rupees.
• Predicting if a user would like to listen to a newly released song or not based on historical data.
• Predicting the confirmation probability (in fraction) of your train ticket whose current status is waiting list based on historical data.
• Predicting if a patient has diabetes or not based on historical medical records.
• Predicting if a customer is satisfied or unsatisfied from the product purchased from an ecommerce website using the reviews he/she wrote for the purchased product.

Answer: A, C

Q4. Which of the following is an unsupervised learning task?

• Group audio files based on the language of the speakers.
• Group applicants to a university based on their nationality.
• Predict a student’s performance in the final exams.
• Predict the trajectory of a meteorite.

Answer: A, B

Q5. Which of the following is a categorical feature?

• Number of rooms in a hostel.
• Gender of a person.
• Your weekly expenditure in rupees.
• Ethnicity of a person.
• Area (in sq. centimeter) of your laptop screen.
• The color of the curtains in your room.
• Number of legs an animal has.
• Minimum RAM requirement (in GB) of a system to play a game like FIFA, DOTA.

Answer: B, D, F

Q6. Which of the following is a reinforcement learning task?

• Learning to drive a cycle
• Learning to predict stock prices
• Learning to play chess
• Learning to predict spam labels for e-mails

Q7. 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)

• None of the above

Answer: 5/8

Q8. Find the mean of 0-1 loss for the given predictions:

Answer: 0.5

Q9. Which of the following statements are true? Check all that apply.

• A model with more parameters is more prone to overfitting and typically has higher variance.
• If a learning algorithm is suffering from high bias, only adding more training examples may not improve the test error significantly.
• When debugging learning algorithms, it is useful to plot a learning curve to understand if there is a high bias or high variance problem.
• If a neural network has much lower training error than test error, then adding more layers will help bring the test error down because we can fit the test set better.

Answer: A, B, C

Q10. Bias and variance are given by:

• E[f^(x)]−f(x),E[(E[f^(x)]−f^(x))2]
• E[f^(x)]−f(x),E[(E[f^(x)]−f^(x))]2
• (E[f^(x)]−f(x))2,E[(E[f^(x)]−f^(x))2]
• (E[f^(x)]−f(x))2,E[(E[f^(x)]−f^(x))]2

Answer: E[f^(x)]−f(x),E[(E[f^(x)]−f^(x))2]

Session: JAN-APR 2023

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

• Predicting rainfall based on historical data.
• Predicting if a customer is going to return or keep a particular product he/she purchased from e-commerce website based on the historical data about the customer purchases and the particular product.

Answer: b, c, d, e

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

• Predicting the temperature (in Celsius) of a room from other environmental features (such as atmospheric pressure, humidity, etc).
• Predicting if a cricket player is a batsman or bowler given his playing records.
• Predicting the price of a house (in INR) based on the data consisting prices of other houses (in INR) and its features such as area, number of rooms, location, etc.
• Filtering of spam messages.
• Predicting the weather for tomorrow as “hot,” “cold,” or “rainy” based on the historical data wind speed, humidity, temperature, and precipitation.

Answer: a, c

Q3) Which of the following is a regression task?

• Predicting the confirmation probability (in fraction) of your train ticket whose current status is the waiting list based on historical data.
• Predicting if a customer is satisfied or unsatisfied with the product purchased from an e-commerce website using the reviews he/she wrote for the purchased product.

Q4) Which of the following is an unsupervised task?

• Predicting if a new edible item is sweet or spicy based on the information of the ingredients, their quantities, and labels (sweet or spicy) for many other similar dishes.
• Grouping of hand-written digits from their image.
• Predicting the time (in days) a PhD student will take to complete his/her thesis to earn a degree based on the historical data such as qualifications, department, institute, research area, and time taken by other scholars to earn the degree.
• All of the above.

Answer: b, c

Q5) Which of the following is a categorical feature?

• Area (in sq. centimeters) of your laptop screen.

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

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

• 3+i√7/2, 3−i√7/2, where i=√−1
• 3+i√7/4, 3−i√7/4, where i=√−1
• 3+i√7, 3−i√7, where i=√−1
• Can be computed only if A is a symmetric matrix.
• Cannot be computed as the entries of the matrix A are not given.

Answer: e. 3+i√7, 3−i√7, where i=√−1

Q8) What happens when your model complexity increases?

• Model Bias decreases
• Model Bias increases
• Variance of the model decreases
• Variance of the model increases

Answer: a, d

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?

• Underfitted models have high bias.
• Underfitted models have low bias.
• Overfitted models have low variance.
• Overfitted models have high variance.

Session: JUL-DEC 2022

1. Which of the following are supervised learning problems? (multiple may be correct)

• a. Learning to drive using a reward signal.
• b. Predicting disease from blood sample.
• c. Grouping students in the same class based on similar features.
• d. Face recognition to unlock your phone.

Answer: b, d

2. Which of the following are classification problems? (multiple may be correct)

• a. Predict the runs a cricketer will score in a particular match.
• b. Predict which team will win a tournament.
• c. Predict whether it will rain today.
• d. Predict your mood tomorrow.

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

• a. Predict the price of a house 10 years after it is constructed.
• b. Predict if a house will be standing 50 years after it is constructed.
• c. Predict the weight of food wasted in a restaurant during next month.
• d. Predict the sales of a new Apple product.

Answer: a, c, d

4. Which of the following is an unsupervised learning task? (multiple options may be correct)

• a. Group audio files based on language of the speakers.
• b. Group applicants to a university based on their nationality.
• c. Predict a student’s performance in the final exams.
• d. Predict the trajectory of a meteorite.

Answer: a, b

5. Given below is your dataset. You are using KNN regression with K=3. What is the prediction for a new input value (3, 2)?

Answer: 2.50

6. Which of the following is a reinforcement learning task? (multiple options may be correct)

Answer: a, b, c

7. Find the mean of squared error for the given predictions:

8. Find the mean of 0-1 loss for the given predictions:

9. Bias and variance are given by:

10. Which of the following are true about bias and variance? (multiple options may be correct)

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• 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
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• Download Videos
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• Handouts (1)

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PYQ NPTEL Introduction To Machine Learning – IITKGP Assignment Answers 2022

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NPTEL Introduction to Machine Learning Assignment 2 Answers 2022

• by QuizXp Team
• January 30, 2022 February 22, 2022

Are you looking for the Answers to NPTEL Introduction to Machine Learning Assignment 2 – IIT Madras? This article will help you with the answer to the  Nation al Programme on Technology Enhanced Learning  ( NPTEL )  Course “ NPTEL Introduction to Machine Learning Assignment 2 “

What is Introduction to Machine Learning?

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.

CRITERIA TO GET A CERTIFICATE

Average assignment score = 25% of the average of 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

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF THE 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.

Below you can find the answers for NPTEL Introduction to Machine Learning Assignment 2

NPTEL Introduction to Machine Learning Assignment 2 Answers:-

Q1. 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

(A) 10000 × 17 (B) 10002 × 17 (C) 10000 × 18 (D) 10000 × 19

Q2. 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 )=Σ pi =1| β | and Ω2( β )=Σ pi =1 β 2 Which one is more likely to result in sparse weights?

(A) Ω1 (B) Ω2 (C) Both Ω1 and Ω2 will result in sparse weights  (D) Neither of Ω1 or Ω2 can result in sparse weights

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Q3. The model obtained by applying linear regression on the identified subset of features may differ from the model obtained at the end of the process of identifying the subset during

(A) Forward stepwise selection (B) Backward stepwise selection (C) Forward stagewise selection (D) All of the above

Q4. Consider forward selection, backward selection and best subset selection with respect to the same data set. Which of the following is true?

(A) Best subset selection can be computationally more expensive than forward selection (B) Forward selection and backward selection always lead to the same result  (C) Best subset selection can be computationally less expensive than backward selection (D) Best subset selection and forward selection are computationally equally expensive

(E)Both (b) and (d)

???? Next Week Answers: Assignment 03 ????

Q5. In the lecture on Multivariate Regression, you learn about using orthogonalization iteratively to obtain regression co-effecients. This method is generally referred to as Multiple Regression using Successive Orthogonalization. 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 ?

(A) No. Doing so will result in the wrong γ matrix. and hence, the wrong βi ’s.

(B) Yes. Since zj −1 is orthogonal to zj − l ∀ l ≤ j 1, the multiple regression in each iteration is essentially a univariate regression on each of the previous residuals. Since the regression coefficients for the previous residuals don’t change over iterations, we can re-use the coefficients for further iterations.

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Q6. Principal Component Regression (PCR) is an approach to find an orthogonal set of basis vectors which can then be used to reduce the dimension of the input. Which of the following matrices contains the principal component directions as its columns (follow notation from the lecture video)

Q7. Consider the following five training examples

We want to learn a function f ( x ) of the form f ( x )= a x + 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.

Let us build a nearest neighbours classifier that will predict which language a word belongs to. Say we represent each word using the following features.• Length of the word• Number of consonants in the word• Whether it ends with the letter ’o’ (1 if it does, 0 if it doesn’t)

For example, the representation of the word ‘waffle’ would be [6, 2, 0]. For a distance function, use the Manhattan distance.

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NPTEL Introduction to Machine Learning Assignment 2 Answers 2022:- All the Answers provided here to help the students as a reference, You must submit your assignment at your own knowledge.

NPTEL Introduction to Machine Learning Assignment 7 Answers

NPTEL Introduction to Machine Learning Assignment 7 Answers 2022:-  In This article, we have provided the answers of Introduction to Machine Learning Assignment 7 You must submit your assignment to your own knowledge.

About Introduction To Machine Learning

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. CRITERIA TO GET A CERTIFICATE Average assignment score = 25% of the average of 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 YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF THE 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.

NPTEL Introduction to Machine Learning Assignment 7 Answers 2022 {July – Dec}

1. You have 2 binary classifiers A and B. A has accuracy=0% and B has accuracy=50%. Which classifier is more useful? a. A b. B c. Both are good d. Cannot say

2. You have 2 multi-class classifiers A and B. A has accuracy=0% and B has accuracy=50%. Which classifier is more useful? a. A b. B c. Both are good d. Cannot say

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3. Using the bootstrap approach for sampling, the new dataset will have _________ of the original samples on expectation. a. 50.0% b. 56.8% c. 63.2% d. 73.6%

4. You have a special case where your data has 10 classes and is sorted according to target labels. You attempt 5-fold cross validation by selecting the folds sequentially. What can you say about your resulting model? a. It will have 100% accuracy. b. It will have 0% accuracy. c. Accuracy will depend on how good the model does. d. Accuracy will depend on the compute power available for training.

5. Given the following information What is the precision and recall? a. 0.5, 0.4375 b. 0.7, 0.636 c. 0.6, 0.636 d. 0.7, 0.4375 e. None of the above

6. AUC for your newly trained model is 0.5. Is your model prediction completely random? a. Yes b. No c. ROC curve is needed to derive this conclusion d. Cannot be determined even with ROC

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7. What is the effect of using bagging on weak classifiers for variance? a. Increases variance b. Reduces variance c. Does not change

8. You are building a model to detect cancer. Which metric will you prefer for evaluating your model? a. Accuracy b. Sensitivity c. Specificity d. MSE

9. You are building a model to detect a mild medical condition for which further testing costs are extremely expensive. Which metric will you prefer for evaluating your model? a. Accuracy b. Sensitivity c. Specificity d. MSE

10. A:  Boosting takes many weak learners and combines them into a strong learner. B:  Boosting determines the proportion of importance each weak learner should be assigned and weighs its prediction by it and combines them to make the final prediction. a. A is True. B is True. B is the correct explanation for A. b. A is True. B is True. B is not the correct explanation for A. c. A is True. B is False. d. Both A and B are False.

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NPTEL Introduction to Machine Learning Assignment 7 Answers 2022 {Jan – June}

Q1.  In LOO Cross Validation, you get K estimators. (excluding the final estimator that may be an ensemble of these K estimators) If size of dataset = N, K =?

• None of the above

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Q2.  Given the following information

What is the precision and recall?

• 0.5, 0.6 
• 0.3, 0.8 
• 0.3, 0.6 
• 0.5, 0.8 

Q3.  To plot ROC curve, you first order the data points in _______ order of their likelihood of being positive.

• Descending 
• Ascending 
• Random 
• Doesn’t matter

Q4.   Which of the following are true?TP – True Positive, TN  –  True Negative, FP – False Positive, FN – False Negative

Answer:- b, c, e

Q5.   Consider the following two statements:

A: In bagging, the estimators can be trained parallel. B: Each estimator in bagging uses the same algorithm.

•  A is True. B is True. B is the correct explanation for A. 
• A is True. B is True. B is  not  the correct explanation for A. 
• A is True. B is False. 
• Both A and B are False.

Q6.   For a binary classification problem, consider the two statements below:

A: A classifier with AUC=0 is the least useful classifier. B: A classifier with AUC=0.5 is the least useful classifier.

{Hint: For A, what if the labels were reversed?}

• A is False. B is True. 
• Both are False. 
• Their ensemble will be the worst classifier.

Q7.  The relationship between their recall is:

• Recall(A) > Recall(B) 
• Recall(A) < Recall(B) 
• Recall(A) = Recall(B) 
• Cannot be determined

Q8.  True/False: Model A is equivalent to a random model based on its confusion matrix.

Q9.  Consider the following two statements:

A: The estimators in Boosting can be trained in parallel. B: Boosting is simply Bagging with a different sample distribution.

• A is True. B is True. B is the correct explanation for A. 

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Nptel introduction to machine learning assignment answers week 8 2022 iitkgp.

Dear students, are you looking for help in Machine Learning NPTEL week 8 assignment answers? So, here in this article, we have provided Machine Learning week 8 assignment answer’s hint.

NPTEL Introduction to Machine Learning Assignment Answers Week 8

Q1. For two runs of K-Mean clustering is it expected to get same clustering results?

a. Yes b. No

Answer: b. No

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Q2. Which of the following can act as possible termination conditions in K-Means?

I.For a fixed number of iterations.

II. Assignment of observations to clusters does not change between iterations. Except for cases with a bad local minimum.

III. Centroids do not change between successive iterations.

IV. Terminate when RSS falls below a threshold

A. I, III and IV

B. I, II and III

C. I, II and IV

D. All of the above

Answer : D. All of the above

Q3. After performing K-Means Clustering analysis on a dataset, you observed the following dendrogram. Which of the following conclusion can be drawn from the dendrogram?

a.There were 28 data points in clustering analysis.

b. The best no. of clusters for the analysed data points is 4.

c. The proximity function used is Average-link clustering.

d. The above dendrogram interpretation is not possible for K-Means clustering analysis.

Answer : d. The above dendrogram interpretation is not possible for K-Means clustering analysis.

Q4. What should be the best choice of no. of clusters based on the following results:

Answer: c. 3

Q5. Given, six points with the following attributes:

Which of the following clustering representations and dendrogram depicts the use of MIN or Single link proximity function in hierarchical clustering:

Answer: Option A

Q6. Is it possible that assignment of observations to clusters does not change between successiveiterations of K-means?

c. Can’t say

d. None of these

Answer: a. Yes

Q7. What is the possible reason(s) for producing two different dendograms using agglomerative clustering for the same data set?

a. Proximity function

b. No. of data points

c. Variables used

d. All of these

Answer: d. All of these

Q8. Which of the following algorithms suffer from the problem of convergence at local optima?

I. K-means clustering

II. Agglomerative clustering

III. Expectation-minimization clustering

IV. Divisive clustering

a. I and II

b. II and III

c. III and IV

d. I and III

Answer: d. I and III

Q9. Which of the following is/are valid iterative strategy before performing clustering analysis for treating missing values?

a. Imputation with mean

b. Nearest neighbour assignment

c. Imputation with expectation-maximization algorithm

Answer : c. Imputation with expectation-maximization algorithm

Q10. If two variables V1 and V2 are used for clustering, which of the following is/are true with K means clustering algorithm for K=3?

I. If V1 and V2 have a correlation of 1, cluster centroid will be in a straight line.

II. If V1 and V2 have a correlation of 0, cluster centroid will be in a straight line.

c. I and II

Answer: a. I only

Disclaimer: These answers are provided only for the purpose to help students to take references. This website does not claim any surety of 100% correct answers. So, this website urges you to complete your assignment yourself.

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NPTEL Introduction to Machine Learning Assignment Answers Week 7

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Introduction to Machine Learning: Open now for exam registration July 2021!!

Dear Candidate, Here is a golden opportunity for those who had previously enrolled in this course during the  Jan 2021  semester, but could not participate in the exams or were absent/did not pass the exam for this course. This course is being reoffered in July 2021 and we are giving you another chance to write the exam in Sep/Oct 2021 and obtain a certificate based on NPTEL norms. Do not let go of this unique opportunity to earn a certificate from the IITs/IISc. IMPORTANT instructions for learners - Please read this carefully 1. The exam date for this course:  October 24, 2021 2. Certification exam registration URL is:  https://examform.nptel.ac.in/     Please fill the exam form using the  same Enrolled email id  & make fee payment via the form, as before. 3. Choose from the Cities where exam will be conducted:   Exam Cities 4. You DO NOT have to re-enroll in the courses.  5. You DO NOT have to resubmit Assignments OR participate in the non-proctored  programming exams. 6. If you do enroll to July 2021 course, we will take the best average assignment scores/non-proctored programming exam score across the two semesters Our suggestion: - Please check once if you have >= 40/100  in average assignment score and also participate in the non-proctored programming exams that will be conducted during this semester in the course to become eligible for the e-certificate, wherever applicable. - If not, please submit Assignments again in the July 2021 course & and also participate in the non-proctored programming exams to become eligible for the e-certificate. - You can also submit Assignments again and participate in the non-proctored programming exams if you want to better your previous scores. RECOMMENDATION:  Please enroll to the July 2021 course and brush up your lessons for the exam. 7.  Exam fees:  If you register for the exam and pay before  Sep 13, 2021, 10:00 AM , Exam fees will be  Rs. 1000/- per exam .  If you register for exam before  Sep 13, 2021, 10:00 AM  and have not paid or if you register between  Sep 13, 2021, 10:00 AM & Sep 17, 2021, 5:00 PM , Exam fees will be  Rs. 1500/-  per exam  8. 50% fee waiver for the 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.  9. Last date for exam registration: Sep 17, 2021, 5:00 PM (Friday).   10. Mode of payment: Online payment - debit card/credit card/net banking.  11.  HALL TICKET:  The hall ticket will be available for download tentatively by  2 weeks prior to the exam date  . We will confirm the same through an announcement once it is published.  12. 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.  13.  Data changes:   Last date for data changes: Sep 17, 2021, 5:00 PM:  All the fields in the Exam form except for the following ones can be changed until the form closes.  The following 6 fields can be changed ONLY when there are NO courses in the course cart. And you will be able to edit the following fields only if you: -  REMOVE unpaid courses from the cart And/or - CANCEL paid courses  1. Do you come under the SC/ST category? *  2. SC/ST Proof  3. Are you a person with disabilities? *  4. Are you a person with disabilities above 40%?  5. Disabilities Proof  6. What is your role ?  Note:  Once you remove or cancel a course, you will be able to edit these fields immediately.  But, for cancelled courses, refund of fees will be initiated only after 2 weeks.  14.  LAST DATE FOR CANCELLING EXAMS and getting a refund: Sep 17, 2021, 5:00 PM   15. Click here to view Timeline and Guideline :  Guideline   Domain Certification Domain Certification helps learners to gain expertise in a specific Area/Domain. This can be helpful for learners who wish to work in a particular area as part of their job or research or for those appearing for some competitive exam or becoming job ready or specialising in an area of study.     Every domain will comprise Core courses and Elective courses. Once a learner completes the requisite courses per the mentioned criteria, you will receive a Domain Certificate showcasing your scores and the domain of expertise. Kindly refer to the following link for the list of courses available under each domain:  https://nptel.ac.in/noc/Domain/discipline.html Thanks & Regards,  NPTEL TEAM

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Introduction to Machine Learning : Week 12 Feedback Form

Introduction to machine learning : week 12 is live now.

Dear students The lecture videos for Week-12 have been uploaded for the course  Introduction to Machine Learning . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=122&lesson=123 Practice Assignment for Week-12 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=122&assessment=143   Assignment for Week-12 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=122&assessment=169 The assignment has to be submitted on or before Wednesday, [14-04-2021, 23:59 IST] .   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. -NPTEL Team

Introduction to Machine Learning : Week 11 Feedback Form

Introduction to machine learning : week 11 is live now.

Dear students The lecture videos for Week-11 have been uploaded for the course  Introduction to Machine Learning . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=117&lesson=118 Practice Assignment for Week-11 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=117&assessment=144   Assignment for Week-11 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=117&assessment=167 The assignment has to be submitted on or before Wednesday, [07-04-2021, 23:59 IST].   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. -NPTEL Team

Introduction to Machine Learning : Assignment 9 Re-evaluation !!

Dear Learners, Re-evaluation has been done by making the weightage as 0 for Question 6 in Assignment 9. Students are requested to find their revised scores of Assignment 9 on the Progress page. Thanks & Regards, NPTEL Team

Introduction to Machine Learning : Week 10 Feedback Form

Introduction to machine learning : assignment 9 reevaluation.

Dear Learner, Assignment 9 submission of all students have been reevaluated by changing the answer for question number 6. Students are requested to find their revised scores of Assignment 9 in the Progress page. Thanks & Regards, -NPTEL Team.

Introduction to Machine Learning : Week 10 is live now!!

Dear students The lecture videos for Week-10 have been uploaded for the course  Introduction to Machine Learning . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=110&lesson=111 Practice Assignment for Week-10 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=110&assessment=142   Assignment for Week-10 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=110&assessment=165 The assignment has to be submitted on or before Wednesday, [31-03-2021, 23:59 IST].   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. -NPTEL Team

Introduction to Machine Learning : Week 9 Feedback Form

Introduction to machine learning : assignment 7 reevaluation.

Dear Learner Assignment 7 submission of all students has been reevaluated after the ignoring question number 2. Students are requested to find their revised scores of Assignment 7 in the Progress page. Thanks & Regards, - NPTEL Team.

Introduction to Machine Learning : Week 9 is live now!!

Dear students The lecture videos for Week-9 have been uploaded for the course  Introduction to Machine Learning . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=103&lesson=104 Practice Assignment for Week-9 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=103&assessment=141   Assignment for Week-9 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=103&assessment=163 The assignment has to be submitted on or before Wednesday, [24-03-2021, 23:59 IST].   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. -NPTEL Team

Dear Learner, Assignment 7 submission of all students has been reevaluated after the ignoring question number 4. Students are requested to find their revised scores of Assignment 7 in the Progress page. Thanks & Regards, -NPTEL Team.

Introduction to Machine Learning : Week 8 Feedback Form

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 link: https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=96&lesson=97 Practice Assignment for Week-8 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=96&assessment=140   Assignment for Week-8 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=96&assessment=162 The assignment has to be submitted on or before Wednesday, [17-03-2021, 23:59 IST] .   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. -NPTEL Team

Introduction to Machine Learning : Week 7 Feedback Form

Introduction to machine learning : week 7 is live now.

Dear students 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/noc21_cs24/unit?unit=87&lesson=88 Practice Assignment for Week-7 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=87&assessment=139   Assignment for Week-7 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=87&assessment=159 The assignment has to be submitted on or before Wednesday, [10-03-2021, 23:59 IST].   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. -NPTEL Team

Introduction to Machine Learning : Week 6 Feedback Form

Introduction to machine learning : week 6 is live now.

Dear students 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/noc21_cs24/unit?unit=76&lesson=77 Practice Assignment for Week-6 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=76&assessment=138   Assignment for Week-6 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=76&assessment=156 The assignment has to be submitted on or before Wednesday, [03-03-2021, 23:59 IST].   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. -NPTEL Team

Introduction to Machine Learning : Week 5 Feedback Form

Introduction to machine learning : assignment 3 reevaluation.

Dear Learner, Assignment 3 submission of all students have been reevaluated by changing the answer for question number 8 . Students are requested to find their revised scores of Assignment 3 in the Progress page. Thanks & Regards, -NPTEL Team.

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

Dear students 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/noc21_cs24/unit?unit=65&lesson=66 Practice Assignment for Week-5 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=65&assessment=137   Assignment for Week-5 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=65&assessment=154 The assignment has to be submitted on or before Wednesday, [24-02-2021, 23:59 IST] .   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. -NPTEL Team

Introduction to Machine Learning : Week 4 Feedback Form

Introduction to machine learning : assignment 1 reevaluation .

Dear Learner, Assignment 1 submission of all students have been reevaluated by adding the answer for question number 1 . Students are requested to find their revised scores of Assignment 1 in the Progress page. Thanks & Regards, -NPTEL Team.

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

Dear students 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/noc21_cs24/unit?unit=55&lesson=56 Practice Assignment for Week-4 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=55&assessment=136   Assignment for Week-4 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=55&assessment=152 The assignment has to be submitted on or before Wednesday, [17-02-2021, 23:59 IST].   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. -NPTEL Team

Introduction to Machine Learning : Feedback on Text Transcripts (English) of NPTEL videos

Dear Learners, We have uploaded the English transcripts for this course already. We would like to hear from you, a quick feedback for the same. Please take a minute to fill out this form. Click here  to fill the form -NPTEL Team

Introduction to Machine Learning : Week 3 Feedback Form

Introduction to machine learning : week 3 is live now.

Dear students 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/noc21_cs24/unit?unit=45&lesson=46 Practice Assignment for Week-3 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=45&assessment=135   Assignment for Week-3 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=45&assessment=148 The assignment has to be submitted on or before Wednesday, [10-02-2021, 23:59 IST] .   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. -NPTEL Team

Introduction to Machine Learning : Assignment 2 due date has been extended!!

Dear Learners, Assignment 2  has been released already and the due date for the assignment has been extended Due date of assignment 2 is  Sunday, 07-02-2021, 23:59 IST Please note that there will not be any extension for the upcoming assignments. Note:  Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately.   Thanks & Regards, -NPTEL Team

Week 2 Feedback Form : Introduction to Machine Learning

Dear Learners, Thank you for continuing with the course and hope you are enjoying it. We would like to know if the expectations with which you joined this course are being met and hence please do take 2 minutes to fill out our weekly feedback form. It would help us tremendously in gauging the learner experience. Here is the link to the form:  https://docs.google.com/forms/d/1dnM4PbDMOxdQO7mUQSpKWNbFNn1OJ9ZLp-Szases-O8/viewform Thanks & Regards -NPTEL team

[NOC21-CS24] Clarification in Q8 of assignment 2

Dear Learner, In the 8th question of assignment-2, the representation vector for the word "Waffle" should be [6,4,0].  The given rules to find the feature vector are correct. In case of any doubt, feel free to ask on the forum. Regards, TAs

Introduction to Machine Learning : Week 2 is live now!!

Dear students The lecture videos for Week-2 have been uploaded for the course  Introduction to Machine Learning . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=34&lesson=35 Practice Assignment for Week-2 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=34&assessment=134   Assignment for Week-2 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=34&assessment=147 The assignment has to be submitted on or before Wednesday, [03-02-2021, 23:59 IST].   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. -NPTEL Team

Week 1 Feedback Form : Introduction to Machine Learning

Introduction to machine learning : week 1 is live now.

Dear students The lecture videos for Week-1 have been uploaded for the course  Introduction to Machine Learning . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=23&lesson=24 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). Practice Assignment for Week-1 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=23&assessment=133   Assignment for Week-1 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=23&assessment=145 The assignment has to be submitted on or before Wednesday, [03-02-2021, 23:59 IST] .   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. -NPTEL Team

NPTEL: Exam Registration is open now for Jan 2021 courses!

Dear Learner,  Here is the much-awaited announcement on registering for the Jan 2021 NPTEL course 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. Please note that Assignments submitted through the exam registered email id ALONE will be taken into consideration towards final consolidated score & certification.  3 .  Date of exam: April 25, 2021 Certification exam registration URL is:  https://examform.nptel.ac. in/   Choose from the Cities where exam will be conducted:  Exam Cities   4. Exam fees:  If you register for the exam and pay before  Mar 8, 2021, 10:00 AM,  Exam fees will be  Rs. 1000/- per exam .  If you register for exam before  Mar 8, 2021 , 10:00 AM  and have not paid or if you register between  Mar 8, 2021, 10:00 AM & Mar 12, 2021, 5:00 PM,  Exam fees will be  Rs. 1500/-  per exam  5. 50% fee waiver for the 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.  6. Last date for exam registration: Mar 12, 2021 5:00 PM (Friday).  7. Mode of payment: Online payment - debit card/credit card/net banking.  8. HALL TICKET:  The hall ticket will be available for download tentatively by  2 weeks prior to the exam date .  We will confirm the same through an announcement once it is published.  9. 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.  10.  Data changes:  Last date for data changes: Mar 12, 2021, 5:00 PM:  All the fields in the Exam form except for the following ones can be changed until the form closes.  The following 6 fields can be changed ONLY when there are NO courses in the course cart. And you will be able to edit the following fields only if you: -  REMOVE unpaid courses from the cart And/or - CANCEL paid courses  1. Do you come under the SC/ST category? *  2. SC/ST Proof  3. Are you a person with disabilities? *  4. Are you a person with disabilities above 40%?  5. Disabilities Proof  6. What is your role ?  Note:  Once you remove or cancel a course, you will be able to edit these fields immediately.  But, for cancelled courses, refund of fees will be initiated only after 2 weeks.  11.  LAST DATE FOR CANCELLING EXAMS and getting a refund: Mar 12, 2021, 5:00 PM  12. Click here to view Timeline and Guideline :  Guideline    Thanks & Regards, NPTEL TEAM

Introduction to Machine Learning : Week 0 is live now!!

Dear Learners,  We welcome you all to this course Introduction to Machine Learning . The assignment 0 has been released.  This assignment is based on prerequisite of the course.  You can find the assignment in the link :  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=16&assessment=132 Due date of assignment 0 is  25-01-2021, 23:59 IST. Please note that this assignment is for practice and it will not be graded . Thanks & Regards  -NPTEL Team

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

Dear Learners, The lecture videos for Week-1 have been uploaded for the course Introduction to Machine Learning . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=23&lesson=24 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).   As we have done so far, please use the discussion forums if you have any questions on this module. - NPTEL Team

Welcome to NPTEL Online Course - Jan 2021!!

• 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 assignments 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: April 25,2021 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.
• Once again, thanks for your interest in our online courses and certification. Happy learning. 

NPTEL : Keep in touch with us via Social Media

Dear Learner You already must know NPTEL is providing course certificates to those who complete the course successfully, with the learning happening right at your home or where you are. But NPTEL also keeps bringing out new initiatives and courses - which we would like to keep you posted on. Click the below links to like and follow us on Social Media for instant Updates: Facebook: https://www.facebook.com/NPTELNoc Twitter: https://twitter.com/nptelindia Linkedin: https://www.linkedin.com/in/nptel-india-085866ba/ Instagram: https://www.instagram.com/swayam_nptel/  Like and Follow us on Social Media. Let's create a better future by learning and growing together.  -NPTEL Team.

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