# NPTEL Introduction to Machine Learning Assignment 1 Answers 2023

Hello NPTEL Learners, In this article, you will find NPTEL Introduction to Machine Learning Assignment 1 Week 1 Answers 2023. All the Answers are provided below to help the students as a reference don’t straight away look for the solutions, first try to solve the questions by yourself. If you find any difficulty, then look for the solutions.

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## NPTEL Introduction to Machine Learning Assignment 1 Answers 2023:

#### Q.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 rainfall based on historical data
• 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.
• Fingerprint recognition of a particular person used in biometric attendance from the fingerprint data of various other people and that particular person

#### Q.2. Which of the following is not a classification problem?

• Predicting the temperature (in Celsius) of a room from other environmental features (such as atmospheric pressure, humidity etc).
• Predicting if a cricket player is a batsman or bowler given his playing records.
• 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.
• Filtering of spam messages
• Predicting the weather for tomorrow as “hot”, “cold”, or “rainy” based on the historical data wind speed, humidity, temperature, and precipitation.

#### Q.3. Which of the following is a regression task? (multiple options may be correct)

• 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 e-commerce website using the the reviews he/she wrote for the purchased product.

#### Q.4. Which of the following is an unsupervised task?

• 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.
• Grouping related documents from an unannotated corpus.
• Grouping of hand-written digits from their image.
• 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.
• all of the above

#### Q.5.Which of the following is a categorical feature?

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

#### Q.6. 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

#### Q.8. What happens when your model complexity increases? (multiple options may be correct)

• Model Bias decreases
• Model Bias increases
• Variance of the model decreases
• Variance of the model increases

• 0.136
• 0.160
• 0.360
• 0.840
• 0.773
• 0.573
• 0.181

#### Q.10. Which of the following are false about bias and varianceof overfitted and underfitted models? (multiple options may be correct)

• Underfitted models have high bias.
• Underfitted models have low bias.
• Overfitted models have low variance.
• Overfitted models have high variance.
##### NPTEL Introduction to Machine Learning Assignment 1 Answers Join Group👇

Disclaimer: This answer is provided by us only for discussion purpose if any answer will be getting wrong don’t blame us. If any doubt or suggestions regarding any question kindly comment. The solution is provided by Chase2learn. This tutorial is only for Discussion and Learning purpose.

#### About NPTEL Introduction to Machine Learning 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.

##### Course Outcome:
• 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.

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