NPTEL Introduction to Machine Learning Week 1 Assignment Answers 2025
1. Which of the following is/are unsupervised learning problem(s)?
- Sorting a set of news articles into four categories based on their titles
- Forecasting the stock price of a given company based on historical data
- Predicting the type of interaction (positive/negative) between a new drug and a set of human proteins
- Identifying close-knit communities of people in a social network
- Learning to generate artificial human faces using the faces from a facial recognition dataset
Answer :- For Answers Click Here
2. Which of the following statement(s) about Reinforcement Learning (RL) is/are true?
- While learning a policy, the goal is to maximize the reward for the current time step
- During training, the agent is explicitly provided the most optimal action to be taken in each state.
- The actions taken by an agent do no affect the environment in any way.
- RL agents used for playing turn based games like chess can be trained by playing the agent against itself (self play).
- RL can be used in a autonomous driving system.
Answer :-
3. Which of the following is/are regression tasks(s)?
- Predicting whether an email is spam or not spam
- Predicting the number of new CoVID cases in a given time period
- Predicting the total number of goals a given football team scores in an year
- Identifying the language used in a given text document
Answer :-
4. Which of the following is/are classification task(s)?
- Predicting whether or not a customer will repay a loan based on their credit history
- Forecasting the weather (temperature, humidity, rainfall etc.) at a given place for the following 24 hours
- Predict the price of a house 10 years after it is constructed.
- Predict if a house will be standing 50 years after it is constructed.
Answer :-
5. Consider the following dataset. Fit a linear regression model of the form y=β0+β1x1+β2x2
using the mean-squared error loss. Using this model, the predicted value of y at the point (x1,x2) = (0.5, −1.0) is
- 4.05
- 2.05
- −1.95
- −3.95
Answer :-
6. Consider the following dataset. Using a k-nearest neighbour (k-NN) regression model with k = 3, predict the value of y at (x1,x2) = (1.0, 0.5). Use the Euclidean distance to find the nearest neighbours.
- −1.766
- −1.166
- 1.133
- 1.733
Answer :- For Answers Click Here
7. Consider the following dataset with three classes: 0, 1 and 2. x1 and x2 are the independent variables whereas y is the class label. Using a k-NN classifier with k = 5, predict the class label at the point (x1,x2) = (1.0, 1.0). Use the Euclidean distance to find the nearest neighbours.
- 0
- 1
- 2
- Cannot be predicted
Answer :-
8. Consider the following statements regarding linear regression and k-NN regression models. Select the true statements.
- A linear regressor requires the training data points during inference.
- A k-NN regressor requires the training data points during inference.
- A k-NN regressor with a higher value of k is less prone to overfitting.
- A linear regressor partitions the input space into multiple regions such that the prediction over a given region is constant.
Answer :-
9.
Answer :-
10.
- On a given training dataset, the mean-squared error of (i) is always less than or equal to that of (ii).
- (i) is likely to have a higher variance than (ii).
- (ii) is likely to have a higher variance than (i).
- If (i) overfits the data, then (ii) will definitely overfit.
- If (ii) underfits the data, then (i) will definitely underfit.
Answer :- For Answers Click Here
