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## NPTEL Introduction to Machine Learning Assignment 3 Answers 2022

- by QuizXp Team
- February 10, 2022 February 22, 2022

Are you looking for the Answers to NPTEL Introduction to Machine Learning Assignment 3 – 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 3 “

## 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 3

Assignment No. | Answers |
---|---|

Assignment 1 | |

Assignment 2 | |

Assignment 3 | |

Assignment 4 | |

Assignment 5 | |

Assignment 6 | |

Assignment 7 | |

Assignment 8 |

## NPTEL Introduction to Machine Learning Assignment 3 Answers:-

Q1. consider the case where two classes follow Gaussian distribution which are centered at (6, 8) and (−6, −4) and have identity covariance matrix. Which of the following is the separating decision boundary using LDA assuming the priors to be equal?

Q2. Which of the following are differences between PCR and LDA?

Q3. Which of the following are differences between LDA and Logistic Regression?

Q4. We have two classes in our dataset. The two classes have the same mean but different variance .

???? Next Week Answers: Assignment 04 ????

Q5. We have two classes in our dataset. The two classes have the same variance but different mean .

Q6. Which of these techniques do we use to optimise Logistic Regression:

Q7. Suppose we have two variables, X and Y (the dependent variable), and we wish to find their relation. An expert tells us that relation between the two has the form Y = meX + c . Suppose the samples of the variables X and Y are available to us. Is it possible to apply linear regression to this data to estimate the values of m and c ?

Q8. What might happen to our logistic regression model if the number of features is more than the number of samples in our dataset?

Q9. Logistic regression also has an application in

Q10. Consider the following datasets:

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NPTEL Introduction to Machine Learning Assignment 3 Answers 2022:- All the Answers provided here to help the students as a reference, You must submit your assignment at your own knowledge.

- 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)

Module Name | Download | Description | Download Size |
---|---|---|---|

Linear Regression | Linear Algebra Tutorial | 192 |

Sl.No | Chapter Name | MP4 Download |
---|---|---|

1 | A brief introduction to machine learning | |

2 | Supervised Learning | |

3 | Unsupervised Learning | |

4 | Reinforcement Learning | |

5 | Probability Basics - 1 | |

6 | Probability Basics - 2 | |

7 | Linear Algebra - 1 | |

8 | Linear Algebra - 2 | |

9 | Statistical Decision Theory - Regression | |

10 | Statistical Decision Theory - Classification | |

11 | Bias-Variance | |

12 | Linear Regression | |

13 | Multivariate Regression | |

14 | Subset Selection 1 | |

15 | Subset Selection 2 | |

16 | Shrinkage Methods | |

17 | Principal Components Regression | |

18 | Partial Least Squares | |

19 | Linear Classification | |

20 | Logistic Regression | |

21 | Linear Discriminant Analysis 1 | |

22 | Linear Discriminant Analysis 2 | |

23 | Linear Discriminant Analysis 3 | |

24 | Optimization | |

25 | Perceptron Learning | |

26 | SVM - Formulation | |

27 | SVM - Interpretation & Analysis | |

28 | SVMs for Linearly Non Separable Data | |

29 | SVM Kernels | |

30 | SVM - Hinge Loss Formulation | |

31 | Weka Tutorial | |

32 | Early Models | |

33 | Backpropogation I | |

34 | Backpropogation II | |

35 | Initialization, Training & Validation | |

36 | Maximum Likelihood Estimate | |

37 | Priors & MAP Estimate | |

38 | Bayesian Parameter Estimation | |

39 | Introduction | |

40 | Regression Trees | |

41 | Stopping Criteria & Pruning | |

42 | Loss Functions for Classification | |

43 | Categorical Attributes | |

44 | Multiway Splits | |

45 | Missing Values, Imputation & Surrogate Splits | |

46 | Instability, Smoothness & Repeated Subtrees | |

47 | Tutorial | |

48 | Evaluation Measures I | |

49 | Bootstrapping & Cross Validation | |

50 | 2 Class Evaluation Measures | |

51 | The ROC Curve | |

52 | Minimum Description Length & Exploratory Analysis | |

53 | Introduction to Hypothesis Testing | |

54 | Basic Concepts | |

55 | Sampling Distributions & the Z Test | |

56 | Student\'s t-test | |

57 | The Two Sample & Paired Sample t-tests | |

58 | Confidence Intervals | |

59 | Bagging, Committee Machines & Stacking | |

60 | Boosting | |

61 | Gradient Boosting | |

62 | Random Forest | |

63 | Naive Bayes | |

64 | Bayesian Networks | |

65 | Undirected Graphical Models - Introduction | |

66 | Undirected Graphical Models - Potential Functions | |

67 | Hidden Markov Models | |

68 | Variable Elimination | |

69 | Belief Propagation | |

70 | Partitional Clustering | |

71 | Hierarchical Clustering | |

72 | Threshold Graphs | |

73 | The BIRCH Algorithm | |

74 | The CURE Algorithm | |

75 | Density Based Clustering | |

76 | Gaussian Mixture Models | |

77 | Expectation Maximization | |

78 | Expectation Maximization Continued | |

79 | Spectral Clustering | |

80 | Learning Theory | |

81 | Frequent Itemset Mining | |

82 | The Apriori Property | |

83 | Introduction to Reinforcement Learning | |

84 | RL Framework and TD Learning | |

85 | Solution Methods & Applications | |

86 | Multi-class Classification |

Sl.No | Chapter Name | English |
---|---|---|

1 | A brief introduction to machine learning | |

2 | Supervised Learning | |

3 | Unsupervised Learning | |

4 | Reinforcement Learning | |

5 | Probability Basics - 1 | |

6 | Probability Basics - 2 | |

7 | Linear Algebra - 1 | |

8 | Linear Algebra - 2 | |

9 | Statistical Decision Theory - Regression | |

10 | Statistical Decision Theory - Classification | |

11 | Bias-Variance | |

12 | Linear Regression | |

13 | Multivariate Regression | |

14 | Subset Selection 1 | |

15 | Subset Selection 2 | |

16 | Shrinkage Methods | |

17 | Principal Components Regression | |

18 | Partial Least Squares | |

19 | Linear Classification | |

20 | Logistic Regression | |

21 | Linear Discriminant Analysis 1 | |

22 | Linear Discriminant Analysis 2 | |

23 | Linear Discriminant Analysis 3 | |

24 | Optimization | |

25 | Perceptron Learning | |

26 | SVM - Formulation | |

27 | SVM - Interpretation & Analysis | |

28 | SVMs for Linearly Non Separable Data | |

29 | SVM Kernels | |

30 | SVM - Hinge Loss Formulation | |

31 | Weka Tutorial | |

32 | Early Models | |

33 | Backpropogation I | |

34 | Backpropogation II | |

35 | Initialization, Training & Validation | |

36 | Maximum Likelihood Estimate | |

37 | Priors & MAP Estimate | |

38 | Bayesian Parameter Estimation | |

39 | Introduction | |

40 | Regression Trees | |

41 | Stopping Criteria & Pruning | |

42 | Loss Functions for Classification | |

43 | Categorical Attributes | |

44 | Multiway Splits | |

45 | Missing Values, Imputation & Surrogate Splits | |

46 | Instability, Smoothness & Repeated Subtrees | |

47 | Tutorial | |

48 | Evaluation Measures I | |

49 | Bootstrapping & Cross Validation | |

50 | 2 Class Evaluation Measures | |

51 | The ROC Curve | |

52 | Minimum Description Length & Exploratory Analysis | |

53 | Introduction to Hypothesis Testing | |

54 | Basic Concepts | |

55 | Sampling Distributions & the Z Test | |

56 | Student\'s t-test | |

57 | The Two Sample & Paired Sample t-tests | |

58 | Confidence Intervals | |

59 | Bagging, Committee Machines & Stacking | |

60 | Boosting | |

61 | Gradient Boosting | |

62 | Random Forest | |

63 | Naive Bayes | |

64 | Bayesian Networks | |

65 | Undirected Graphical Models - Introduction | |

66 | Undirected Graphical Models - Potential Functions | |

67 | Hidden Markov Models | |

68 | Variable Elimination | |

69 | Belief Propagation | |

70 | Partitional Clustering | |

71 | Hierarchical Clustering | |

72 | Threshold Graphs | |

73 | The BIRCH Algorithm | |

74 | The CURE Algorithm | |

75 | Density Based Clustering | |

76 | Gaussian Mixture Models | |

77 | Expectation Maximization | |

78 | Expectation Maximization Continued | |

79 | Spectral Clustering | |

80 | Learning Theory | |

81 | Frequent Itemset Mining | |

82 | The Apriori Property | |

83 | Introduction to Reinforcement Learning | |

84 | RL Framework and TD Learning | |

85 | Solution Methods & Applications | |

86 | Multi-class Classification |

Sl.No | Language | Book link |
---|---|---|

1 | English | |

2 | Bengali | Not Available |

3 | Gujarati | Not Available |

4 | Hindi | Not Available |

5 | Kannada | Not Available |

6 | Malayalam | Not Available |

7 | Marathi | Not Available |

8 | Tamil | Not Available |

9 | Telugu | Not Available |

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## SIKSHAPATH Latest Articles

Nptel introduction to machine learning assignment answers week 3 2022 iitkgp.

Are you looking for help in Machine Learning NPTEL Week 3 Assignment Answers? So, here in this article, we have provided Machine Learning week 3 Assignment Answer’s hint.

## NPTEL Introduction to Machine Learning Assignment Answers Week 3

Q1. Suppose, you have given the following data where x and y are the 2 input variables and Class is the dependent variable.

X | Y | Class |
---|---|---|

-1 | 1 | – |

0 | 1 | + |

0 | 2 | – |

1 | -1 | – |

1 | 0 | + |

1 | 2 | + |

2 | 2 | – |

2 | 3 | + |

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

Answer: a. + Class

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Q2. Imagine you are dealing with a 10 class classification problem. What is the maximum number of discriminant vectors that can be produced by LDA?

Answer : c. 9

Q3. Fill in the blanks:

K-Nearest Neighbor is a ________,_______ algorithm.

a. Non-parametric, eager

b. Parametric, eager

c. Non-parametric, lazy

d. Parametric, lazy

Answer: c. Non-parametric, lazy

Q4. 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.

Answer: a. KNN algorithm does more computation on test time rather than train time.

Q5. 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

Answer: d. All of the above

Q6. When there is noise in data, which of the following options would improve the performance 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

Answer: a. Increase the value of k

Q7. Find the value of the Pearson’s correlation coefficient of X and Y from the data in the following table.

AGE (X) | GLUCOSE (Y) |
---|---|

43 | 99 |

21 | 65 |

25 | 79 |

42 | 75 |

d. 0.33

Answer: b. 0.68

Q8. 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 orthogonal to each other

Answer: a. PCA is a supervised method

Q9. 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

Answer : a. Similar users

Q10. Identify whether the following statement is true or false? “PCA can be used for projecting and visualizing data in lower dimensions.”

Answer: a. TRUE

(in one click) |

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.

Also Available:

NPTEL Introduction to Machine Learning Assignment Answers Week 2

NPTEL Introduction to Machine Learning Assignment Answers Week 4

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## Course list

## Introduction to Machine Learning

₹ 3,000.00

Prof. Balaraman Ravindran IIT Madras

*Additional GST and optional Exam fee are applicable.

## Description

Certification process, course details.

- Reviews (2)

With the increased availability of data from varied sources there has been increasing attention paid to the various data driven disciplines such as analytics and machine learning. In this course we intend to introduce some of the basic concepts of machine learning from a mathematically well motivated perspective. We will cover the different learning paradigms and some of the more popular algorithms and architectures used in each of these paradigms.

## INTENDED AUDIENCE

This is an elective course. Intended for senior UG/PG students. BE/ME/MS/PhD

## PREREQUISITES

We will assume that the students know programming for some of the assignments.If the students have done introductory courses on probability theory and linear algebra it would be helpful. We will review some of the basic topics in the first two weeks as well.

## INDUSTRY SUPPORT

Any company in the data analytics/data science/big data domain would value this course.

## ABOUT THE INSTRUCTOR

Prof. Balaraman Ravindran is currently an Professor in Computer Science at IIT Madras and Mindtree Faculty Fellow . He has nearly two decades of research experience in machine learning and specifically reinforcement learning. Currently his research interests are centered on learning from and through interactions and span the areas of data mining, social network analysis, and reinforcement learning.

1. Join the course Learners may pay the applicable fees and enrol to a course on offer in the portal and get access to all of its contents including assignments. Validity of enrolment, which includes access to the videos and other learning material and attempting the assignments, will be mentioned on the course. Learner has to complete the assignments and get the minimum required marks to be eligible for the certification exam within this period.

COURSE ENROLMENT FEE: The Fee for Enrolment is Rs. 3000 + GST

2. Watch Videos+Submit Assignments After enrolling, learners can watch lectures and learn and follow it up with attempting/answering the assignments given.

3. Get qualified to register for exams A learner can earn a certificate in the self paced course only by appearing for the online remote proctored exam and to register for this, the learner should get minimum required marks in the assignments as given below:

CRITERIA TO GET A CERTIFICATE Assignment score = Score more than 50% in at least 9/12 assignments. Exam score = 50% of the proctored certification exam score out of 100 Only the e-certificate will be made available. Hard copies will not be dispatched.”

4. Register for exams The certification exam is conducted online with remote proctoring. Once a learner has become eligible to register for the certification exam, they can choose a slot convenient to them from what is available and pay the exam fee. Schedule of available slot dates/timings for these remote-proctored online examinations will be published and made available to the learners.

EXAM FEE: The remote proctoring exam is optional for a fee of Rs.1500 + GST. An additional fee of Rs.1500 will apply for a non-standard time slot.

5. Results and Certification After the exam, based on the certification criteria of the course, results will be declared and learners will be notified of the same. A link to download the e-certificate will be shared with learners who pass the certification exam.

CERTIFICATE TEMPLATE

Week 1: Introduction: Statistical Decision Theory – Regression, Classification, Bias Variance Week 2: Linear Regression, Multivariate Regression, Subset Selection, Shrinkage Methods, Principal Component Regression, Partial Least squares Week 3: Linear Classification, Logistic Regression, Linear Discriminant Analysis Week 4: Perceptron, Support Vector Machines Week 5: Neural Networks – Introduction, Early Models, Perceptron Learning, Backpropagation, Initialization, Training & Validation, Parameter Estimation – MLE, MAP, Bayesian Estimation Week 6: Decision Trees, Regression Trees, Stopping Criterion & Pruning loss functions, Categorical Attributes, Multiway Splits, Missing Values, Decision Trees – Instability Evaluation Measures Week 7: Bootstrapping & Cross Validation, Class Evaluation Measures, ROC curve, MDL, Ensemble Methods – Bagging, Committee Machines and Stacking, Boosting Week 8: Gradient Boosting, Random Forests, Multi-class Classification, Naive Bayes, Bayesian Networks Week 9: Undirected Graphical Models, HMM, Variable Elimination, Belief Propagation Week 10: Partitional Clustering, Hierarchical Clustering, Birch Algorithm, CURE Algorithm, Density-based Clustering Week 11: Gaussian Mixture Models, Expectation Maximization Week 12: Learning Theory, Introduction to Reinforcement Learning, Optional videos (RL framework, TD learning, Solution Methods, Applications)

## BOOKS AND REFERENCES:

- The Elements of Statistical Learning, by Trevor Hastie, Robert Tibshirani, Jerome H. Friedman (freely available online)
- Pattern Recognition and Machine Learning, by Christopher Bishop (optional)

## 2 reviews for Introduction to Machine Learning

biswajit – March 11, 2022

this is a very good course.

CHETHAN MS – October 14, 2022

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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.

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?

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?

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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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

👇 For Week 04 Assignment Answers 👇

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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## What is Moscow Russia's land mark?

The Kremlin is one of the most famous Russian landmarks, as is Red Square. Mount Elbrus is also a landmark, as it is one of the Seven Summits of the world. Another landmark is the Trans-Siberian Railway that goes from Moscow to Vladivostok, bordering China and North Korea .

the palace of basin, World War 2 began there

The Kremlin is one of the most famous Russian landmarks, as is Red Square. Mount Elbrus is also a landmark, as it is one of the Seven Summits of the world.

Some landmarks found in Russia are the Ural Mountains and large plains areas. There are also forests, and a large arctic tundra region.

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General discussion thread - weekend of december 23, 2022.

If you have a random or short question, thought, observation, or theory, this is the thread for that. If you have a more thorough theory, please post in the pinned Theories Thread . This helps us keep the front page clearer for news, updates, and more in-depth discussion posts.

This thread is sorted by new, so the newest submission is on top. Treat each top level comment as if it were its own text post on the sub. If you prefer to read the most upvoted comments first, you can switch to sort by "best" (on mobile, this can be done by clicking the icon to the left of the three dots above the post heading).

Please review our recent rule reminders and updates post if you missed it or are new to this community.

Recent News

December 22, 2022 - No significant case updates, but some interesting content:

Reason - University of Idaho Murders Yield Libel Lawsuit Against "Internet Sleuth" - for discussion, see u/Fuzzywuzzy2930 's post here

Video: News Nation: Moscow Police Chief Insists Early Investigations Were Properly Done | Dan Abrams Live - for discussion, see u/m0rningview420 's post here

Video: Law & Crime Interview with Moscow PD Spokesperson Aaron Snell - for discussion, see u/Kitkat0y 's post here

Huffington Post - Police Chief Defends Idaho Slayings Probe Amid Criticism From Victim's Family - for discussion, see u/tsagdiyev 's post here , or a similar article cited and discussed in u/shimmy_hey 's post here

December 21, 2022 - Fox: University Adding More Campus Security Personnel in Spring Semester

For discussion, see u/tsagdiyev 's post here

December 20, 2022 - MPD Press Release PDF Warning

Investigators are aware of a Hyundai Elantra located in Eugene, Oregon and have spoken with the owner. The vehicle was involved in a collision and subsequently impounded. The vehicle is registered out of Colorado and the female owner is not believed to have any relation to any property in Moscow, Idaho or the ongoing murder investigations. The public is asked to stop contacting the owner.

As required by law, several Moscow Police body camera videos were requested and released through the public records request process. The original release of any video by this agency is the official version, and circulated videos cannot be verified as authentic or unedited.

Rumor Control: Another video, believed to be taken on the night of the murders at a local downtown business, is known to investigators. Investigators have identified an individual called “Adam” in the video and he is cooperating with detectives.

Related Video: 12-20-22 Video Update with Chief Fry

"There have been numerous questions about leadership in this investigation. Let me be clear, this is the Moscow Police Departments' investigation, and I am the Chief of Police. The decisions are mine and mine alone. I have an excellent Command Staff, with over 90 years of combined experience, overseeing the investigation's daily operation, and I select who runs the investigative teams," said Moscow Police Chief James Fry. "We are supported by highly trained and experienced personnel from the Idaho State Police and the FBI. Their continued resources and knowledge are vital to our success. Our investigative units work under a unified structure and have the autonomy to move forward and solve this case. Despite statements about my team, we remain focused on solving the murder of four students to seek justice for them, their families and to help our community heal."

For discussion of the 12/20/2022 MPD updates, see u/salmon450 's post here , u/CXT_LXDY 's post here , or u/musicforasushigrl 's post here

December 20, 2022 - Investigators returned to the King Road house carrying a black case. Brian Entin reported they were inside for about twenty minutes and he saw them through the window on the second floor.

For discussion, see u/s_xo23 's post here

December 18, 2022 - Newly available body camera footage from Moscow PD response to September 1, 2022, noise complaint at 1122 King Road - video here

For discussion, see u/Antony_NOW 's post here ; or u/Brilliant_Football95 's post here ; or relatedly, u/pumpkinspicecum 's post about what might be gleaned about locks on the doors in the residence here

December 17, 2022 - Fox News: Man Mentioned in Newly Obtained Video Not a Suspect, Victim's Father Says

Fox obtained additional video of Kaylee, Maddie, and hoodie guy walking downtown Moscow (before arriving at the food truck)

Fox shared a still from the video along with the audio, which Fox describes as: "Maddie, what did you say to Adam?" a woman asks as the group walks under an outdoor surveillance camera. "Like, I told Adam everything," the second woman replies.

In an interview with Fox, Steve Goncalves said police had the recently released footage and described it as "just two girls having a good time, asking about their bartender. . . . . It was pretty clear that this individual was not part of the investigation as far as a suspect."

Related: News Nation releases the full video with audio

December 16, 2022 - Fox News: Investigators Traveled at Least 24 Miles to Collect Surveillance Video Related to the Killings

Investigators traveled at least 24 miles to the east of Moscow, Idaho, to the nearby towns of Troy and Kendrick to acquire surveillance video;

Investigators asked for video from a gas station in Troy, Idaho, about 12 miles east of Moscow on Highway 8 - but the property does not have a camera facing outside;

Also requested (and received) video from the Food City store in Kendrick, about 24 miles east of Moscow.

For discussion of this article, see u/8psee_247 's post here

Moscow Police King Road Homicides Informational Page and FAQ

Idaho Statesman - How Did Things Unfold Before, After University of Idaho Killings? A Timeline of Events (Updated Dec. 1, 2022)

Moscow Police Daily Activity Log

Xana Kernodle Obituary Xana Kernodle Go Fund Me Xana Kernodle Scholarship Fund

Ethan Chapin Obituary Ethan Chapin Go Fund Me Ethan Chapin Memorial Scholarship Fund

Madison Mogen Obituary Kaylee Goncalves Obituary Madison Mogen and Kaylee Goncalves Go Fund Me

STATUS: At this time, there are no named suspects, no arrests have been made, and no weapon has been found.

MOSCOW POLICE TIP LINES:

Telephone: (208) 883-7180;

Email: [email protected]

Digital Media: fbi.gov/moscowidaho

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## NPTEL: Exam Registration is open now for July 2022 courses!

Dear Candidate, Here is a golden opportunity for those who had previously enrolled in this course during the Jan 2022 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 2022 and we are giving you another chance to write the exam in October 2022 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 29 2022 2. CLICK HERE to register for the exam. 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 in the previous semester 6. If you do enroll to July 2022 course, we will take the best average assignment scores/non-proctored programming exam score across the two semesters NOTE: Please check once if you have >= 40/100 in average assignment score and also participated and satisfied the criteria in the non-proctored programming exams that were conducted in Jan 2022 to become eligible for the e-certificate, wherever applicable. If not, please submit assignments again in the July 2022 course & and also participate in the non-proctored programming exams to become eligible for the e-certificate. We will not be having new assignments or unproctored exams in the previous semester's(Jan 2022) course. You can also submit assignments again and participate in the non-proctored programming exams if you want to take fresh assignments or need to improve your previous scores. RECOMMENDATION: If you want to take new assignments and an unproctored exam or brush up on your lessons for the exam, please enroll in the July 2022 course. LINK to enroll in the current course: https://onlinecourses.nptel.ac.in/noc22_cs73/preview 7. Exam fees: If you register for the exam and pay before Sep 12, 2022, 10:00 AM , Exam fees will be Rs. 1000/- per exam. If you register for exam before Sep 12, 2022, 10:00 AM and have not paid or if you register between Sep 12, 2022, 10:00 AM & Sep 16, 2022, 10:00 AM , 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 16, 2022, 10:00 AM (Friday). 10. Mode of payment: Online payment - debit card/credit card/net banking/UPI. 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 16, 2022, 10:00 AM: 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 16, 2022, 10:00 AM 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/domains Outside India Candidates Candidates who are residing outside India may also fill the exam form and pay the fees. Mode of exam and other details will be communicated to you separately. Thanks & Regards, NPTEL TEAM

## 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 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. For any further queries please write to [email protected] . - Team NPTEL

## Introduction to Machine Learning : Result Published!

Dear Candidate, The exam scores and E Certificates have been released for April 2022 Exam(s). Step 1 - Are the results of my courses released? Please check the Results published courses list in the below links.:- April 2022 Exam - Click here Step 2 - How to check Results? Please login to internalapp.nptel.ac.in/ . and check your exam results. Use the same login credentials as used to register to the exam. What's next? Please read the pass criteria carefully and check against what you have gotten. If you still have any issues, please report the same here. internalapp.nptel.ac.in/ . We will reply within a week. Last date to report queries: 3 days within publishing of scores. Note : Hard copies of certificates will not be dispatched. The duration shown in the certificate will be based on the timeline of offering of the course in 2022, irrespective of which Assignment score that will be considered. Thanks and Best wishes. NPTEL Team

## Introduction to Machine Learning : Final Feedback Form

Dear student, We are glad that you have attended the NPTEL online certification course. We hope you found the NPTEL Online course useful and have started using NPTEL extensively. In this regard, we would like to have feedback from you regarding our course and whether there are any improvements, you would like to suggest. We are enclosing an online feedback form and would request you to spare some of your valuable time to input your observations. Your esteemed input will help us in serving you better. The link to give your feedback is: https://docs.google.com/forms/d/15gYAWbZsdhuRv_xhXY1DhZRrwN9tPgZ3osGbFxzWgTk/viewform We thank you for your valuable time and feedback. Thanks & Regards, -NPTEL Team

## NPTEL April Exams

Dear Leaner,

_NPTEL Team

## Introduction to Machine Learning : Assignment 12 Solutions Released!!

Dear Participants, The Solutions of Week 12 for the course "Introduction to Machine Learning" have been released in the portal. Please go through the solution and in case of any doubt post your queries in the forum. Assignment 12 Solution Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=123&lesson=185 Happy Learning! Thanks & Regards, NPTEL Team

## Introduction to Machine Learning : Reevaluation!!

Dear Learners, Assignment 7 submission of all students has been reevaluated by making the weightage as 0 question number 4. Assignment 8 submission of all students has been reevaluated by changing the answer for question number 4. Assignment 10 submission of all students has been reevaluated by changing the answer for question number 2. Assignment 11 submission of all students has been reevaluated by making the weightage as 0 question number 3. Students are requested to find their revised scores of Assignments on the Progress page. -NPTEL Team

## Hall Tickets for April Exams are released !

- If there are any mistakes in the hall ticket such as another person's photo/sign/name, please fill the following Google form & mark the corrections.
- GForm Link - https://forms.gle/aqeMCN15MpnTQPpV9 (Deadline - April 22, 2022 at 11.00 AM)
- These corrections will be reflected in your e-certificate only.
- No changes will be made in the hall ticket.
- We will check and verify the same and send an email confirmation.
- You will still be allowed to write the exam. We will NOT make any changes in the hall ticket issued to you. Please come to the exam centre with the printout of the same hall ticket, along with a valid govt. approved photo id card.
- If the photo/sign/name is yours, we WILL NOT upload any updated photo/sign, etc.
- Requests for changes in exam city, exam center, session, or course will NOT be entertained.

## Introduction to Machine Learning : Assignment 11 Solutions Released!!

Dear Participants, The Solutions of Week 11 for the course "Introduction to Machine Learning" have been released in the portal. Please go through the solution and in case of any doubt post your queries in the forum. Assignment 11 Solution Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=117&lesson=184 Happy Learning! Thanks & Regards, NPTEL Team

## Introduction to Machine Learning : Week 12 Feedback Form

Dear Learner 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/1ya5e9i3ZZYT9O-wbvcEvq3o6Fehl26bvj-hDo1-rsrM/viewform Thank you. -NPTEL team

## Introduction to Machine Learning : Week 12 content is live now !!

Dear Learners, 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: Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=123&lesson=124 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 12 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=123&assessment=146 Assignment-12 for Week 12 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=123&assessment=183 The assignment has to be submitted on or before Wednesday,[20/04/2022], 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. Thanks and Regards, -NPTEL Team

## Introduction to Machine Learning : Assignment 10 Solutions Released!!

Dear Participants, The Solutions of Week 10 for the course "Introduction to Machine Learning" have been released in the portal. Please go through the solution and in case of any doubt post your queries in the forum. Assignment 10 Solution Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=109&lesson=180 Happy Learning! Thanks & Regards, NPTEL Team

## Introduction to Machine Learning : Week 11 Feedback Form

Introduction to machine learning : week 11 content is live now .

Dear Learners, 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: Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=117&lesson=118 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 11 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=117&assessment=145 Assignment-11 for Week 11 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=117&assessment=181 The assignment has to be submitted on or before Wednesday,[13/04/2022], 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. Thanks and Regards, -NPTEL Team

## Introduction to Machine Learning : Assignment 9 Solutions Released!!

Dear Participants, The Solutions of Week 9 for the course "Introduction to Machine Learning" have been released in the portal. Please go through the solution and in case of any doubt post your queries in the forum. Assignment 9 Solution Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=101&lesson=179 Happy Learning! Thanks & Regards, NPTEL Team

## Exam Format - April 24, 2022

Dear Candidate, ****This is applicable only for the exam registered candidates**** Type of exam will be available in the list: Click Here 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 hall ticket is yet to be released . We will notify the same through email and SMS. Type of exam: Computer based exam (Please check in the above list corresponding to your course name) 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. Type of exam: Paper and pen Exam (Please check in the above list corresponding to your course name) The questions will be on the computer. You will have to write your answers on sheets of paper and submit the answer sheets. Papers will be sent to the faculty for evaluation. 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. -NPTEL Team

## Introduction to Machine Learning : Week 10 Feedback Form

Introduction to machine learning : week 10 content is live now .

Dear Learners, 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: Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=109&lesson=110 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 10 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=109&assessment=144 Assignment-9 for Week 10 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=109&assessment=177 The assignment has to be submitted on or before Wednesday,[06/04/2022], 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. Thanks and Regards, -NPTEL Team

## Introduction to Machine Learning : Assignment 8 Solutions Released!!

Dear Participants, The Solutions of Week 8 for the course "Introduction to Machine Learning" have been released in the portal. Please go through the solution and in case of any doubt post your queries in the forum. Assignment 8 Solution Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=93&lesson=178 Happy Learning! Thanks & Regards, NPTEL Team

## RBCDSAI Teaching Fellowship Program - REGISTER NOW !!

## Introduction to Machine Learning : Week 9 Feedback Form

Dear Learner 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/1ya5e9i3ZZYT9O-wbvcEvq3o6Fehl26bvj-hDo1-rsrM/viewform Thank you -NPTEL team

## Introduction to Machine Learning - Assignment 4 and 6 Reevaluation !!

Dear Learner, Submission of all students has been reevaluated by changing the answer for questions: Assignment 4 - Questions 2 and 5 Assignment 6 - Question 6 Students are requested to find their revised scores of Assignments 4 and 6 on the Progress page. -NPTEL Team.

## Introduction to Machine Learning - Week-9 content is live now !!

Dear Learners, 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: Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=101&lesson=102 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-9 for Week-9 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=101&assessment=143 Assignment-9 for Week-9 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=101&assessment=176 The assignment has to be submitted on or before Wednesday,[30/03/2022], 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. Thanks and Regards, -NPTEL Team

## Introduction to Machine Learning - Assignment 7 Solutions Released!!

Dear Participants, The Solutions of Week 7 for the course "Introduction to Machine Learning" have been released in the portal. Please go through the solution and in case of any doubt post your queries in the forum. Assignment 7 Solution Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=83&lesson=173 Happy Learning! Thanks & Regards, NPTEL Team

## Introduction to Machine Learning : Week 8 Feedback Form

Introduction to machine learning - week-8 content is live now .

Dear Learners, 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: Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=93&lesson=94 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-8 for Week-8 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=93&assessment=142 Assignment-8 for Week-8 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=93&assessment=174 The assignment has to be submitted on or before Wednesday,[23/03/2022], 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. Thanks and Regards, -NPTEL Team

## Introduction to Machine Learning - Assignment 6 Solutions Released!!

Dear Participants, The Solutions of Week 6 for the course " Introduction to Machine Learning " have been released in the portal. Please go through the solution and in case of any doubt post your queries in the forum. Assignment 6 Solution Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=71&lesson=171 Happy Learning! Thanks & Regards, NPTEL Team

## Introduction to Machine Learning : Week 7 Feedback Form

Introduction to machine learning - week-7 content is live now .

Dear Learners, 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: Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=83&lesson=84 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-7 for Week-7 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=83&assessment=141 Assignment-7 for Week-7 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=83&assessment=172 The assignment has to be submitted on or before Wednesday,[16/03/2022], 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. Thanks and Regards, -NPTEL Team

## Introduction to Machine Learning - Assignment 5 Solutions Released!!

Dear Participants, The Solutions of Week 5 for the course "Introduction to Machine Learning" have been released in the portal. Please go through the solution and in case of any doubt post your queries in the forum. Assignment 5 Solution Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=61&lesson=170 Happy Learning! Thanks & Regards, NPTEL Team

## Introduction to Machine Learning : Week 6 Feedback Form

Introduction to machine learning - week-6 content is live now .

Dear Learners, 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: Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=71&lesson=72 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-6 for Week-6 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=71&assessment=140 Assignment-6 for Week-6 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=71&assessment=169 The assignment has to be submitted on or before Wednesday,[09/03/2022], 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. Thanks and Regards, -NPTEL Team

## Introduction to Machine Learning - Assignment 4 Solutions Released!!

Dear Participants, The Solutions of Week 4 for the course "Introduction to Machine Learning" have been released in the portal. Please go through the solution and in case of any doubt post your queries in the forum. Assignment 4 Solution Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=52&lesson=166 Happy Learning! Thanks & Regards, NPTEL Team

## Introduction to Machine Learning : Week 5 Feedback Form

Introduction to machine learning : week 4 feedback form, introduction to machine learning - week-5 content is live now .

Dear Learners, 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: Link: https://onlinecourses.nptel.ac.in/noc22_cs29/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). Practice Assignment-5 for Week-5 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=61&assessment=139 Assignment-5 for Week-5 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=61&assessment=168 The assignment has to be submitted on or before Wednesday,[02/03/2022], 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. Thanks and Regards, -NPTEL Team

## Introduction to Machine Learning - Assignment 3 Solutions Released!!

Dear Participants, The Solutions of Week 3 for the course " Introduction to Machine Learning " have been released in the portal. Please go through the solution and in case of any doubt post your queries in the forum. Assignment 3 Solution Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=43&lesson=165 Happy Learning! Thanks & Regards, NPTEL Team

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

Dear Learners, 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: Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=52&lesson=53 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-4 for Week-4 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=52&assessment=138 Assignment-4 for Week-4 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=52&assessment=167 The assignment has to be submitted on or before Wednesday,[23/02/2022], 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. Thanks and Regards, -NPTEL Team

## Introduction to Machine Learning - Assignment 1 Solutions Released!!

Dear Participants, The Solutions of Week 1 for the course " Introduction to Machine Learning " have been released in the portal. Please go through the solution and in case of any doubt post your queries in the forum. Assignment 1 Solution Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=23&lesson=163 Happy Learning! Thanks & Regards, NPTEL Team

## Introduction to Machine Learning - Assignment 2 Solutions Released!!

Dear Participants, The Solutions of Week 2 for the course " Introduction to Machine Learning " have been released in the portal. Please go through the solution and in case of any doubt post your queries in the forum. Assignment 2 Solution Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=33&lesson=164 Happy Learning! Thanks & Regards, NPTEL Team

## Assignments for Week 1 & 2 due on Feb 9 2022 !!

Dear Learner, Assignments for Week 1 & 2 are open for submission .The last date for submission is Feb 9 2022 IST 23:59 . If you have not submitted the assignments ,kindly submit the same before the due date. -NPTEL Team.

## Introduction to Machine Learning : Week 3 Feedback Form

Introduction to machine learning - week-3 content is live now .

Dear Learners, 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: Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=43&lesson=44 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-3 for Week-3 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=43&assessment=137 Assignment-3 for Week-3 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=43&assessment=162 The assignment has to be submitted on or before Wednesday,[16/02/2022], 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. Thanks and Regards, -NPTEL Team

## Introduction to Machine Learning : Week 2 Feedback Form

Introduction to machine learning - week-2 content is live now .

Dear Learners, 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: Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=33&lesson=34 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-2 for Week-2 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=33&assessment=136 Assignment-2 for Week-2 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=33&assessment=161 The assignment has to be submitted on or before Wednesday,[09/02/2022], 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. Thanks and Regards, -NPTEL Team

## Introduction to Machine Learning : Week 1 Feedback Form

Dear Learner 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/1ya5e9i3ZZYT9O-wbvcEvq3o6Fehl26bvj-hDo1-rsrM/viewform Thank you. -NPTEL team

## Introduction to Machine Learning - Week-1 content is 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: Link: https://onlinecourses.nptel.ac.in/noc22_cs29/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-1 for Week-1 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=23&assessment=135 Assignment-1 for Week-1 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=23&assessment=160 The assignment has to be submitted on or before Wednesday,[09/02/2022], 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. Thanks and Regards, -NPTEL Team

## Attention: Regarding the Social media Groups

Dear Learners, The discussion forum, which is embedded on the course portal, is the only authentic medium to communicate regarding this course . This Forum is monitored by the Faculty coordinator and team and on which we will respond. NPTEL is NOT responsible for any whatsapp group or any other group created in any social media platform. Request the learners to refrain from sharing phone numbers and other details which may be misused as this is a public group and this information is available to all members in this group. This kind of activity is strictly prohibited and NPTEL will not be responsible for misuse of any such information. All the best, Happy Learning! -NPTEL Team

## Introduction to Machine Learning : Week 1 content is 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: Link: https://onlinecourses.nptel.ac.in/noc22_cs29/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). Assignment will be released shortly. As we have done so far, please use the discussion forums if you have any questions on this module. Thanks and Regards, -NPTEL Team

## Introduction to Machine Learning : Assignment 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 a prerequisite of the course. You can find the assignment in the link : https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=16&assessment=147 Please note that this assignment is for practice and it will not be graded. Thanks & Regards -NPTEL Team

## NPTEL: Exam Registration is open now for Jan 2022 courses!

Dear Learner,

Here is the much-awaited announcement on registering for the Jan 2022 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 24, 2022

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 March 14, 2022, 10:00 AM, Exam fees will be Rs. 1000/- per exam .

If you register for exam before March 14, 2022, 10:00 AM and have not paid or if you register between March 14, 2022, 10:00 AM & March 18, 2022, 10:00 AM, 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: March 18, 2022 10:00 AM (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: March 18, 2022 10:00 AM :

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: March 18, 2022 10:00 AM

12. 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,

## Introduction to Machine Learning: Welcome to NPTEL Online Course - Jan 2022!!

- 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 24, 2022 Morning session 9am to 12 noon; Afternoon Session 2 pm to 5 pm.
- 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.

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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.

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Introduction to Machine Learning - IITKGP - - Announcements. NPTEL: Exam Registration is open now for July 2022 courses! Dear Candidate, Here is a golden opportunity for those who had previously enrolled in this course during the July 2021 semester, but could not participate in the exams or were absent/did not pass the exam for this course.

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There will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Date: February 24, 2023 - Friday. Time:04.30 PM - 06.30 PM.

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Introduction to Machine Learning - Assignment 4 and 6 Reevaluation !! Dear Learner, Submission of all students has been reevaluated by changing the answer for questions: Assignment 4 - Questions 2 and 5 Assignment 6 - Question 6 Students are requested to find their revised scores of Assignments 4 and 6 on the Progress page.-NPTEL Team.