Nptel Introduction to Machine Learning Week 1 Assignment Answer

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

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

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

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

Nptel Introduction to Machine Learning Week 1 Assignment Answer

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)

Nptel Introduction to Machine Learning Week 1 Assignment Answer

Week 1 : Assignment 1

Due date: 2025-02-05, 23:59 IST.

Assignment not submitted

1 point

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

1 point

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.

1 point

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

1 point

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.

1 point

Consider the following dataset. Fit a linear regression model of the form $y = β_{0} + β_{1} x_{1} + β_{2} x_{2}$ using the mean-squared error loss. Using this model, the predicted value of $y$ at the point ( $x_{1}, x_{2}$ ) = (0.5, −1.0) is

4.05

2.05

−1.95

−3.95

1 point

Consider the following dataset. Using a k-nearest neighbour (k-NN) regression model with $k$ = 3, predict the value of $y$ at ( $x_{1}, x_{2}$ ) = (1.0, 0.5). Use the Euclidean distance to find the nearest neighbours.

−1.766

−1.166

1.133

1.733

1 point

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 ( $x_{1}, x_{2}$ ) = (1.0, 1.0). Use the Euclidean distance to find the nearest neighbours.

Cannot be predicted

1 point

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.

1 point

Which of the following statement(s) regarding bias and variance is/are correct?

$Bias = E [{(E [\hat{f} (x)] - \hat{f} (x))}^{2}]; Variance = E [{(\hat{f} (x) - f (x))}^{2}]$

$Bias = E [\hat{f} (x)] - f (x); Variance = E [{(E [\hat{f} (x)] - \hat{f} (x))}^{2}]$

Low bias and high variance is a sign of overfitting

Low variance and high bias is a sign of overfitting

Low variance and high bias is a sign of underfitting

1 point

Suppose that we train two kinds of regression models corresponding to the following equations.

(i). $y = β_{0} + β_{1} x_{1} + β_{2} x_{2}$
(ii). $y = β_{0} + β_{1} x_{1} + β_{2} x_{2} + β_{3} x_{1} x_{2} + β_{4} x_{1}^{2} + β_{5} x_{2}^{2}$

Which of the following statement(s) is/are correct?

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.

Nptel Introduction to Machine Learning Week 1 Assignment Answer

Nptel Introduction to Machine Learning Week 1 Assignment Answer

Course layout

Nptel Introduction to Machine Learning Week 1 Assignment Answer

Week 1 : Assignment 1

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