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 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.

Answer:-b, d

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

a. Predict the runs a cricketer will score in a particular match.

b. Predict which team will win a tournament.

c. Predict whether it will rain today.

d. Predict your mood tomorrow.

Answer:-b, c, d

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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.

Answer:-a, c, d

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

a. Group audio files based on language of the speakers.

b. Group applicants to a university based on their nationality.

c. Predict a student’s performance in the final exams.

d. Predict the trajectory of a meteorite.

Answer:-a, b

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

Answer:-d

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

Answer:-a, c

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

Answer:-a

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

Answer:-d

👇**For Week 02 Assignment Answers**👇

**9. Bias and variance are given by:**

Answer:-a

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

Answer:-b, d

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Introduction to Machine Learning | Answers |

Assignment 1 | Click Here |

Assignment 2 | Click Here |

Assignment 3 | Click Here |

Assignment 4 | Click Here |

Assignment 5 | Click Here |

Assignment 6 | Click Here |

Assignment 7 | Click Here |

Assignment 8 | Click Here |

Assignment 9 | Click Here |

Assignment 10 | Click Here |

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.

**Answer:- a, c**

**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

**Answer:- f**

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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**

**Answer:- b**

**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

Answer:- g

**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.

**Answer:- a, d**

**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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