# INTRODUCTION TO MACHINE LEARNING Week 1

**Session: JULY-DEC 2023**

**Course Name: Introduction to Machine Learning**

**Course Link: Click Here**

**These are Introduction to Machine Learning Week 1 Assignment 1 Answers**

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

Grouping related documents from an unannotated corpus.

Predicting credit approval based on historical data.

Predicting if a new image has cat or dog based on the historical data of other images of cats and dogs, where you are supplied the information about which image is cat or dog.

Fingerprint recognition of a particular person used in biometric attendance from the fingerprint data of various other people and that particular person.

**Answer: B, C, D**

**Q2. Which of the following are classification problems?**

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

Predict which team will win a tournament.

Predict whether it will rain today.

Predict your mood tomorrow.

**Answer: B, C, D**

**Q3. Which of the following is a regression task?**

Predicting the monthly sales of a cloth store in rupees.

Predicting if a user would like to listen to a newly released song or not based on historical data.

Predicting the confirmation probability (in fraction) of your train ticket whose current status is waiting list based on historical data.

Predicting if a patient has diabetes or not based on historical medical records.

Predicting if a customer is satisfied or unsatisfied from the product purchased from ecommerce website using the the reviews he/she wrote for the purchased product.

**Answer: A, C**

**These are Introduction to Machine Learning Week 1 Assignment 1 Answers**

**Q4. 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.

**Answer: A, B**

**Q5. Which of the following is a categorical feature?**

Number of rooms in a hostel.

Gender of a person

Your weekly expenditure in rupees.

Ethnicity of a person

Area (in sq. centimeter) of your laptop screen.

The color of the curtains in your room.

Number of legs an animal.

Minimum RAM requirement (in GB) of a system to play a game like FIFA, DOTA.

**Answer: B, D, F**

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

Learning to drive a cycle

Learning to predict stock prices

Learning to play chess

Leaning to predict spam labels for e-mails

**Answer: A, C**

**These are Introduction to Machine Learning Week 1 Assignment 1 Answers**

**Q7. Let X and Y be a uniformly distributed random variable over the interval [0,4] and [0,6] respectively. If X and Y are independent events, then compute the probability, P(max(X,Y)>3)**

1/6

5/6

2/3

1/2

2/6

5/8

None of the above

**Answer: 5/8**

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

1

0

1.5

0.5

**Answer: 0.5**

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

A model with more parameters is more prone to overfitting and typically has higher variance.

If a learning algorithm is suffering from high bias, only adding more training examples may not improve the test error significantly.

When debugging learning algorithms, it is useful to plot a learning curve to understand if there is a high bias or high variance problem.

If a neural network has much lower training error than test error, then adding more layers will help bring the test error down because we can fit the test set better.

**Answer: B, D**

**These are Introduction to Machine Learning Week 1 Assignment 1 Answers**

**Q10. Bias and variance are given by:**

E[f^(x)]−f(x),E[(E[f^(x)]−f^(x))2]

E[f^(x)]−f(x),E[(E[f^(x)]−f^(x))]2

(E[f^(x)]−f(x))2,E[(E[f^(x)]−f^(x))2]

(E[f^(x)]−f(x))2,E[(E[f^(x)]−f^(x))]2

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

**These are Introduction to Machine Learning Week 1 Assignment 1 Answers**

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**Session: JAN-APR 2023**

**Introduction to Machine Learning Week 1 Assignment 1 Answers**

**Course Name: Introduction to Machine Learning**

**Course Link: Click Here**

**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**

**These are Introduction to Machine Learning Week 1 Assignment 1 Answers**

**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**

**These are Introduction to Machine Learning Week 1 Assignment 1 Answers**

**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**

**These are Introduction to Machine Learning Week 1 Assignment 1 Answers**

**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: b, c**

**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**

**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

c. 2/3

d. 1/2

e. 2/6

f. 5/8

g. None of the above

**Answer: f. 5/8**

**These are Introduction to Machine Learning Week 1 Assignment 1 Answers**

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

a. 3+i√7/2,3−i√7/√2, where √=√−1

b. 1,3

c. 3+i√7/4,3−√7/4, where i=√−1

d. 1/2,3/2

e. 3+i√7,3−i√7, where i=√−1

f. 2,8

g. None of the above

h. Can be computed only if A is a symmetric matrix.

i. Can be computed only if A is a symmetric matrix.

j. Can not be computed as the entries of the matrix A are not given.

**Answer: e. 3+i√7,3−i√7, where i=√−1**

**Q8) What happens when your model complexity increases? (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**

**These are Introduction to Machine Learning Week 1 Assignment 1 Answers**

**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. 0.181**

**These are Introduction to Machine Learning Week 1 Assignment 1 Answers**

**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: b, c**

**These are Introduction to Machine Learning Week 1 Assignment 1 Answers**

#### Introduction to Machine Learning Week 1 Assignment 1 Answers

**Session: JUL-DEC 2022**

Link to Enroll: Click Here

**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**

**These are Introduction to Machine Learning Week 1 Assignment 1 Answers**

**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**

**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**

**These are Introduction to Machine Learning Week 1 Assignment 1 Answers**

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

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

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

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

d. Predict the trajectory of a meteorite.

**Answer: a, b**

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

**Answer: 2.50**

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

**Answer: a, b , c**

**These are Introduction to Machine Learning Week 1 Assignment 1 Answers**

**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**

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

**Answer: b**

**These are Introduction to Machine Learning Week 1 Assignment 1 Answers**

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

**Answer: b ,d**

**These are Introduction to Machine Learning Week 1 Assignment 1 Answers**

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