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## NPTEL Introduction To Machine Learning IITKGP ASSIGNMENT 1 Answers 2022

- July 16, 2022 July 28, 2022

NPTEL Introduction To Machine Learning IITKGP ASSIGNMENT 1 Answers: – Hello students in this article we are going to share NPTEL Introduction To Machine Learning – IITKGP assignment week 1 answers. All the Answers provided below to help the students as a reference, You must submit your assignment at your own knowledge.

## About Introduction To Machine Learning IITKGP Course:-

This course provides a concise introduction to the fundamental concepts in machine learning and popular machine learning algorithms. We will cover the standard and most popular supervised learning algorithms including linear regression, logistic regression, decision trees, k-nearest neighbour, an introduction to Bayesian learning and the naïve Bayes algorithm, support vector machines and kernels and neural networks with an introduction to Deep Learning. We will also cover the basic clustering algorithms. Feature reduction methods will also be discussed. We will introduce the basics of computational learning theory.

## Criteria to get 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.

Certificate will have your name, photograph and the score in the final exam with the breakup.It will have the logos of NPTEL and IIT Kharagpur.It will be e-verifiable at nptel.ac.in/noc.

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Below you can find NPTEL INTRODUCTION TO MACHINE LEARNING IIT KGP Assignment 1 Answers

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## NPTEL Introduction To Machine Learning IITKGP ASSIGNMENT 1 Answers 2022 :-

1. Which of the following are classification tasks? A. Find the gender of a person by analyzing his writing style B. Predict the price of a house based on floor area, number of rooms etc. C. Predict the temperature for the next day D. Predict the number of copies of a book that will be sold this month

2. Which of the following is a not categorical feature? A. Gender of a person B. Height of a person C. Types of Mountains

3. Which of the following tasks is NOT a suitable machine learning task? A. Finding the shortest path between a pair of nodes in a graph B. Predicting if a stock price will rise or fall C. Predicting the price of petroleum D. Grouping mails as spams or non-spams

4. Suppose I have 10,000 emails in my mailbox out of which 200 are spams. The spam detection system detects 150 mails as spams, out of which 50 are actually spams. What is the precision and recall of my spam detection system? A. Precision 33.333%, Recall 25% B. Precision = 25%. Recall 33.33% C. Precision= 33.33%. Recall = 75% D. Precision 75%, Recall = 33.33%

5. A feature F1 can take certain values: A, B, C, D, E, F and represents the grade of students from a college. Which of the following statements is true in the following case? A. Feature F1 is an example of a nominal variable. B. Feature F1 is an example of ordinal variables. C. It doesn’t belong to any of the above categories. D. Both of these

6. One of the most common uses of Machine Learning today is in the domain of Robotics. Robotic tasks include a multitude of ML methods tailored towards navigation, robotic control and a number of other tasks. Robotic control includes controlling the actuators available to the robotic system. An example of this is control of a painting arm in automotive industries. The robotic arm must be able to paint every corner in the automotive parts while minimizing the quantity of paint wasted in the process. Which of the following learning paradigms would you select for training such a robotic arm? A. Supervised learning B. Unsupervised learning C. Combination of supervised and unsupervised learning D. Reinforcement learning

Next Week Assignment Answers

7. How many Boolean functions are possible with n features? A. (22) B. (2) C. (N²) D. (4)

8. What is the use of Validation dataset in Machine Learning? A. To train the machine learning model. B. To evaluate the performance of the machine learning model C. To tune the hyperparameters of the machine learning model D. None of the above

9. Regarding bias and variance, which of the following statements are true? (Here ‘high’ and ‘low’ are relative to the ideal model.) A. Models which overfit have a high bias. B. Models which overfit have a low bias. C. Models which underfit have a high variance. D. Models which underfit have a low variance.

10. Identify whether the following statement is true or false? “Occam’s Razor is an example of Inductive Bias” A. True B. False

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## NPTEL Introduction to Machine Learning Assignment 1 Answers 2023

In this post, We have provided answers of NPTEL Introduction to Machine Learning Assignment 1. We provided answers here only for reference. Plz, do your assignment at your own knowledge.

## NPTEL Introduction To Machine Learning Week 1 Assignment Answer 2023

1. 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.

2. 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.

3. 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 ecommerce website using the the reviews he/she wrote for the purchased product.

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

- Group audio files based on 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.

5. 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 p e rson
- Area (in sq. centimeter) of your laptop screen.
- The color of the curtains in your room.
- Number of legs an animal.
- Minimum RAM requirement (in GB) of a system to play a game like FIFA, DOTA.

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

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

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

- None of the above

9. 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.

10. 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

## NPTEL Introduction to Machine Learning Assignment 1 Answers 2022 [July-Dec]

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.

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.

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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.

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.

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)?

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

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

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

👇 For Week 02 Assignment Answers 👇

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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Assignment 1 | |

Assignment 2 | |

Assignment 3 | |

Assignment 4 | |

Assignment 5 | |

Assignment 6 | |

Assignment 7 | |

Assignment 8 | |

Assignment 9 | |

Assignment 10 | |

Assignment 11 | NA |

Assignment 12 | NA |

## 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.

COURSE LAYOUT

- Week 0: Probability Theory, Linear Algebra, Convex Optimization – (Recap)
- 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)

CRITERIA TO GET A CERTIFICATE

Average assignment score = 25% of 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 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 1 Answers [Jan – June 2022]

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

a. Grouping related documents from an unannotated corpus. b. Predicting credit approval based on historical data c. Predicting rainfall based on historical data d. 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. e. 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 , e

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

a. Predicting the temperature (in Celsius) of a room from other environmental features (such as atmospheric pressure, humidity etc). b.Predicting if a cricket player is a batsman or bowler given his playing records. c. Predicting the price of house (in INR) based on the data consisting prices of other house (in INR) and its features such as area, number of rooms, location etc. d. Filtering of spam messages e. 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? (multiple options may be correct)

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

Q4. Which of the following is an unsupervised task?

a. 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. b. Grouping related documents from an unannotated corpus. c. Grouping of hand-written digits from their image. d. 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. e. all of the above

Answer:- c, d

Q5. Which of the following is a categorical feature?

a. Number of rooms in a hostel. b. Minimum RAM requirement (in GB) of a system to play a game like FIFA, DOTA. c. Your weekly expenditure in rupees. d. Ethnicity of a person e. Area (in sq. centimeter) of your laptop screen. f. The color of the curtains in your room.

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

a. 1/6 b. 5/6 c. 2/3 d. 1/2 e. 2/6 f. 5/8 g. None of the above

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

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

a. Model Bias decreases b. Model Bias increases c. Variance of the model decreases d. 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?

a. 0.136 b. 0.160 c. 0.360 d. 0.840 e. 0.773 f. 0.573 g. 0.181

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

a. Underfitted models have high bias. b. Underfitted models have low bias. c. Overfitted models have low variance. d. Overfitted models have high variance.

NPTEL Introduction to Machine Learning Assignment 1 Answers 2022:- In This article, we have provided the answers of Introduction to Machine Learning Assignment 1.

Disclaimer :- We do not claim 100% surety of solutions, these solutions are based on our sole expertise, and by using posting these answers we are simply looking to help students as a reference, so we urge do your assignment on your own.

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## NPTEL Introduction to Machine Learning Assignment Answers Week 1

Q1. Which of the following are classification tasks?

a. Find the gender of a person by analyzing his writing style b. Predict the price of a house based on floor area, number of rooms etc. c. Predict the temperature for the next day d. Predict the number of copies of a book that will be sold this month

Answer: a. Find the gender of a person by analyzing his writing style

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Q2. Which of the following is a not categorical feature?

a. Gender of a person

b. Height of a person

c. Types of Mountains

d. Nationality of a person

Answer : b. Height of a person

Q3. Which of the following tasks is NOT a suitable machine learning task?

a. Finding the shortest path between a pair of nodes in a graph

b. Predicting if a stock price will rise or fall

c. Predicting the price of petroleum

d. Grouping mails as spams or non-spams

Answer: a. Finding the shortest path between a pair of nodes in a graph

Q4. Suppose I have 10,000 emails in my mailbox out of which 200 are spams. The spam detection system detects 150 mails as spams, out of which 50 are actually spams. What is the precision and recall of my spam detection system?

a. Precision = 33.333%, Recall = 25%

b. Precision 25%, Recall = 33.33%

c. Precision = 33.33%, Recall = 75%

d. Precision = 75%, Recall = 33.33%

Answer: b. Precision 25%, Recall = 33.33%

Q5. A feature F1 can take certain values: A, B, C, D, E, F and represents the grade of students from a college. Which of the following statements is true in the following case?

a. Feature F1 is an example of a nominal variable.

b. Feature F1 is an example of ordinal variables.

c. It doesn’t belong to any of the above categories.

d. Both of these

Answer: b. Feature F1 is an example of ordinal variables.

Q6. One of the most common uses of Machine Learning today is in the domain of Robotics. Robotic tasks include a multitude of ML methods tailored towards navigation, robotic control and a number of other tasks. Robotic control includes controlling the actuators available to the robotic system. An example of this is control of a painting arm in automotive industries. The robotic arm must be able to paint every corner in the automotive parts while minimizing the quantity of paint wasted in the process. Which of the following learning paradigms would you select for training such a robotic arm?

a. Supervised learning

b. Unsupervised learning

c. Combination of supervised and unsupervised learning

d. Reinforcement learning

Answer: d. Reinforcement learning

Q7. How many Boolean functions are possible with n features?

a. (2 2^N )

d. (4 N )

Answer: a. (2 2^N )

Q8. What is the use of Validation dataset in Machine Learning?

a. To train the machine learning model.

b. To evaluate the performance of the machine learning model

c. To tune the hyperparameters of the machine learning model

d. None of the above

Answer: c. To tune the hyperparameters of the machine learning model

Q9. Regarding bias and variance, which of the following statements are true? (Here ‘high’ and ‘low’ are relative to the ideal model.)

a. Models which overfit have a high bias.

b. Models which overfit have a low bias.

c. Models which underfit have a high variance.

d. Models which underfit have a low variance.

Answer: b. Models which overfit have a low bias. c. Models which underfit have a high variance.

Q10. Identify whether the following statement is true or false?

“Occam’s Razor is an example of Inductive Bias”

Answer: a. True

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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
- 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 |

- Review Assignment
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- About the Course
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## RBCDSAI Teaching Fellowship Program - REGISTER NOW !!

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

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 Jan 2022 and we are giving you another chance to write the exam in April 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: April 24, 2022

2. Certification exam registration URL is: CLICK HERE

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 Jan 2022 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 Jan 2022 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 Jan 2022 course and brush up your lessons for the exam.

7. 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

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: March 18, 2022 10:00 AM (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: 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.

14. LAST DATE FOR CANCELLING EXAMS and getting a refund: March 18, 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/noc/Domain/discipline.html

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,

## 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. The discussion forum is being closed though and you cannot ask questions here. For any further queries please write to [email protected] . - Team NPTEL

## Introduction to Machine Learning: Result Published!

- 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 2021, irrespective of which Assignment score that will be considered.

## Feedback for Introduction to Machine Learning

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/1c0IyKJNdR4pyBPYF9Scj7som_yjqOhHcVQulMJb_SSQ/viewform We thank you for your valuable time and feedback. Thanks & Regards, -NPTEL Team

## 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

## April 2021 NPTEL Exams have been postponed!

Dear learner Taking the current covid situation into consideration, the NPTEL exams scheduled to be conducted on 24/25 April stand postponed until further notice. We will keep you informed of the potential dates for the exams as the situation improves and we finalize the same. Thanks and Regards, NPTEL TEAM.

## Exam Format - April 25,2021

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 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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🔊NPTEL Introduction to Machine Learning - IITKGP Week 1 Quiz Assignment Solutions | July 2022This course provides a concise introduction to the fundamental ...

🔊NPTEL Introduction to Machine Learning Week 1 Quiz Assignment Solutions | Jan 2022 | IIT MadrasWith the increased availability of data from varied sources ...

Here's a full videos Solution of the NPTEL Swayam Introduction To Machine Learning- IITKGP Week 1 Assignment 1 answers.#nptelassignmentsolution #nptel2022 #s...

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

Course certificate. 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:24 April 2022Morning session 9am to 12 ...

July 16, 2022. NPTEL INTRODUCTION TO MACHINE LEARNING ASSIGNMENT 1 Answers :- Hello students in this article we are going to share NPTEL The Joy of Computing using Python assignment week 1 answers. All the Answers provided below to help the students as a reference, You must submit your assignment at your own knowledge.

July 16, 2022. NPTEL Introduction To Machine Learning IITKGP ASSIGNMENT 1 Answers: - Hello students in this article we are going to share NPTEL Introduction To Machine Learning - IITKGP assignment week 1 answers. All the Answers provided below to help the students as a reference, You must submit your assignment at your own knowledge.

Answer: d, f. These are Introduction to Machine Learning Week 1 Assignment 1 Answers. 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) a. 1/6. b. 5/6.

None of the above. Answer:- f. NOTE:- Answers of Introduction to Machine Learning Assignment 1 will be uploaded shortly and it will be notified on Telegram, So JOIN NOW. Q7. Let the trace and determinant of a matrix A [acbd] be 6 and 16 respectively. The eigenvalues of A are. Answer:- b.

About Course. This course will provide you access to the all 12 weeks assignment answers of NPTEL Introduction to Machine Learning Subject of Year 2022. Note: Our Answers will be visible for only those who will buy this subject. Buy this course if you have not bought yet.

This course will provide you with access to all 12 weeks of assignment answers for Introduction To Machine Learning - IITKGP. As of now, we have uploaded the answers of Week 1 to 8. Note:- Our answers will be visible to only those who buy this course. Buy this course if you have not yet.

NPTEL ML Assignment Week1 - Free download as PDF File (.pdf), Text File (.txt) or read online for free. This document contains a 10 question multiple choice quiz on machine learning concepts. The questions cover topics like supervised vs unsupervised learning, linear regression, bias and variance in models, precision vs recall, and reinforcement learning.

noc19 cs52 assignment Week 1. swayam NPTEL » Introduction to Machine Learning (IITKGP) Announcements Unit 3 - Week 1 About the Course [email protected] Mentor Ask a Question Progress Course outline How to access the portal Week O Assignment O week 1 Lecture 01 : Introduction Lecture 02 : Different Types of Learning Lecture 03 ...

Dear learners, 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. Session 1: Date: October 18, 2022 - Tuesday.

Answer: b. Feature F1 is an example of ordinal variables. Q6. One of the most common uses of Machine Learning today is in the domain of Robotics. Robotic tasks include a multitude of ML methods tailored towards navigation, robotic control and a number of other tasks. Robotic control includes controlling the actuators available to the robotic ...

Subscribe if you gain some information from this useful video.NPTEL Introduction to Machine Learning Assignment 1 - Unit 3 - Week 1 - Revision for ExamAll Th...

We provide you NPTEL Assignment Answers 2024 and solutions of all courses. Week 1,2,3, 4, 5, 6, 7 , 8, 9, 10 ,11, 1. By Swayam platform.

Assignment 1 Introduction to Machine Learning Prof. B. Ravindran. 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. Sol.

NPTEL provides E-learning through online Web and Video courses various streams. ... Assignments; Download Videos; Transcripts; Books; Handouts (1) ... Linear Algebra: Linear Algebra Tutorial: 192: Sl.No Chapter Name MP4 Download; 1: A brief introduction to machine learning: Download: 2: Supervised Learning: Download: 3: Unsupervised Learning ...

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.

Assignments for Week 1 & 2 due on Feb 9 2022 !! ... Introduction to Machine Learning : Week 2 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 ...

Welcome to the summer 2024 edition of my ranking of the NHL's best prospects at The Athletic.. This two-piece, twice-a-year project ranks the league's top 100 drafted skaters and top 20 ...

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.

🔊NPTEL Introduction to Machine Learning - IITKGP Week 0 Quiz Assignment Solutions | July 2022This course provides a concise introduction to the fundamental ...

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