introduction to machine learning nptel assignment 2 answers 2023

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NPTEL Introduction to Machine Learning Assignment 2 Answers 2023

Hello Learners, In this Post, you will find NPTEL Introduction to Machine Learning Assignment 2 Week 2 Answers 2023 . All the Answers are provided below to help the students as a reference don’t straight away look for the solutions.

NPTEL Introduction to Machine Learning Assignment 3 Answers👇

Note: First try to solve the questions by yourself. If you find any difficulty, then look for the solutions.

NPTEL Introduction to Machine Learning Assignment 2 Answers 2023:

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Q.1. Given a training data set of 10,000 instances, with each input instance having 17 dimensions and each output instance having 2 dimensions, the dimensions of the design matrix used in applying linear regression to this data is

Q.2. suppose we want to add a regularizer to the linear regression loss function, to control the magnitudes of the weights β . we have a choice between ω1(β)=σpi=1|β| and ω2(β)=σpi=1β2 which one is more likely to result in sparse weights.

• Both Ω1 and Ω2 will result in sparse weights
• Neither of Ω1 or Ω2 can result in sparse weights

Q.3. The model obtained by applying linear regression on the identified subset of features may differ from the model obtained at the end of the process of identifying the subset during

• Forward stepwise selection
• Backward stepwise selection
• Forward stagewise selection
• All of the above

NPTEL Introduction to Machine Learning Assignment 3 Answers Join Group👇

Q.4. Consider forward selection, backward selection and best subset selection with respect to the same data set. Which of the following is true?

• Best subset selection can be computationally more expensive than forward selection
• Forward selection and backward selection always lead to the same result
• Best subset selection can be computationally less expensive than backward selection
• Best subset selection and forward selection are computationally equally expensive
• Both (b) and (d)

Q.5. In the lecture on Multivariate Regression, you learn about using orthogonalization iteratively to obtain regression co-effecients. This method is generally referred to as Multiple Regression using Successive Orthogonalization. In the formulation of the method, we observe that in iteration k , we regress the entire dataset on z0,z1,…zk−1 . It seems like a waste of computation to recompute the coefficients for z0 a total of p times, z1 a total of p−1 times and so on. Can we re-use the coefficients computed in iteration j for iteration j+1 for zj−1 ?

• No. Doing so will result in the wrong γ matrix. and hence, the wrong βi ’s.
• Yes. Since zj−1 is orthogonal to zj−l∀l≤j1 , the multiple regression in each iteration is essentially a univariate regression on each of the previous residuals. Since the regression coefficients for the previous residuals don’t change over iterations, we can re-use the coefficients for further iterations.

Q.6. Principal Component Regression (PCR) is an approach to find an orthogonal set of basis vectors which can then be used to reduce the dimension of the input. Which of the following matrices contains the principal component directions as its columns (follow notation from the lecture video)

Nptel introduction to machine learning week 2 answers join group👇, q.7. consider the following five training examples.

We want to learn a function f(x) of the form f(x)=ax+b which is parameterised by (a,b) . Using squared error as the loss function, which of the following parameters would you use to model this function to get a solution with the minimum loss.

Q.8. Here is a data set of words in two languages.

Let us build a nearest neighbours classifier that will predict which language a word belongs to. Say we represent each word using the following features.

• Length of the word

• Number of consonants in the word

• Whether it ends with the letter ’o’ (1 if it does, 0 if it doesn’t)

For example, the representation of the word ‘waffle’ would be [6, 2, 0]. For a distance function, use the Manhattan distance.

d(a,b)=Σni=1|ai−bi|�(�,�)=Σ�=1�|��−��| where a,b∈Rn�,�∈��

Take the input word ‘keto’. With k = 1, the predicted language for the word is?

• None of the above

NPTEL Introduction to Machine Learning Assignment 2 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  Brokenprogrammers . 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 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.

If you have not registered for exam kindly register Through https://examform.nptel.ac.in/

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• Computer Science and Engineering
• NOC:Introduction to Machine Learning(Course sponsored by Aricent) (Video) 
• Co-ordinated by : IIT Madras
• Available from : 2016-01-19
• Intro Video
• A brief introduction to machine learning
• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Probability Basics - 1
• Probability Basics - 2
• Linear Algebra - 1
• Linear Algebra - 2
• Statistical Decision Theory - Regression
• Statistical Decision Theory - Classification
• Bias-Variance
• Linear Regression
• Multivariate Regression
• Subset Selection 1
• Subset Selection 2
• Shrinkage Methods
• Principal Components Regression
• Partial Least Squares
• Linear Classification
• Logistic Regression
• Linear Discriminant Analysis 1
• Linear Discriminant Analysis 2
• Linear Discriminant Analysis 3
• Weka Tutorial
• Optimization
• Perceptron Learning
• SVM - Formulation
• SVM - Interpretation & Analysis
• SVMs for Linearly Non Separable Data
• SVM Kernels
• SVM - Hinge Loss Formulation
• Early Models
• Backpropogation I
• Backpropogation II
• Initialization, Training & Validation
• Maximum Likelihood Estimate
• Priors & MAP Estimate
• Bayesian Parameter Estimation
• Introduction
• Regression Trees
• Stopping Criteria & Pruning
• Loss Functions for Classification
• Categorical Attributes
• Multiway Splits
• Missing Values, Imputation & Surrogate Splits
• Instability, Smoothness & Repeated Subtrees
• Evaluation Measures I
• Bootstrapping & Cross Validation
• 2 Class Evaluation Measures
• The ROC Curve
• Minimum Description Length & Exploratory Analysis
• Introduction to Hypothesis Testing
• Basic Concepts
• Sampling Distributions & the Z Test
• Student\'s t-test
• The Two Sample & Paired Sample t-tests
• Confidence Intervals
• Bagging, Committee Machines & Stacking
• Gradient Boosting
• Random Forest
• Naive Bayes
• Bayesian Networks
• Undirected Graphical Models - Introduction
• Undirected Graphical Models - Potential Functions
• Hidden Markov Models
• Variable Elimination
• Belief Propagation
• Partitional Clustering
• Hierarchical Clustering
• Threshold Graphs
• The BIRCH Algorithm
• The CURE Algorithm
• Density Based Clustering
• Gaussian Mixture Models
• Expectation Maximization
• Expectation Maximization Continued
• Spectral Clustering
• Learning Theory
• Frequent Itemset Mining
• The Apriori Property
• Introduction to Reinforcement Learning
• RL Framework and TD Learning
• Solution Methods & Applications
• Multi-class Classification
• Watch on YouTube
• Assignments
• Download Videos
• Transcripts
• Handouts (1)

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Course list

Introduction to Machine Learning

₹ 3,000.00

Prof. Balaraman Ravindran IIT Madras

*Additional GST and optional Exam fee are applicable.

Description

Additional information, certification process, course details.

• Reviews (2)

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.

ABOUT THE INSTRUCTOR

Prof. Balaraman Ravindran is currently an Professor in Computer Science at IIT Madras and Mindtree Faculty Fellow . He has nearly two decades of research experience in machine learning and specifically reinforcement learning. Currently his research interests are centered on learning from and through interactions and span the areas of data mining, social network analysis, and reinforcement learning.

1. Join the course Learners may pay the applicable fees and enrol to a course on offer in the portal and get access to all of its contents including assignments. Validity of enrolment, which includes access to the videos and other learning material and attempting the assignments, will be mentioned on the course. Learner has to complete the assignments and get the minimum required marks to be eligible for the certification exam within this period.

COURSE ENROLMENT FEE: The Fee for Enrolment is Rs. 3000 + GST

2. Watch Videos+Submit Assignments After enrolling, learners can watch lectures and learn and follow it up with attempting/answering the assignments given.

3. Get qualified to register for exams A learner can earn a certificate in the self paced course only by appearing for the online remote proctored exam and to register for this, the learner should get minimum required marks in the assignments as given below:

CRITERIA TO GET A CERTIFICATE Assignment score = Score more than 50% in at least 9/12 assignments. Exam score = 50% of the proctored certification exam score out of 100 Only the e-certificate will be made available. Hard copies will not be dispatched.”

4. Register for exams The certification exam is conducted online with remote proctoring. Once a learner has become eligible to register for the certification exam, they can choose a slot convenient to them from what is available and pay the exam fee. Schedule of available slot dates/timings for these remote-proctored online examinations will be published and made available to the learners.

EXAM FEE: The remote proctoring exam is optional for a fee of Rs.1500 + GST. An additional fee of Rs.1500 will apply for a non-standard time slot.

5. Results and Certification After the exam, based on the certification criteria of the course, results will be declared and learners will be notified of the same. A link to download the e-certificate will be shared with learners who pass the certification exam.

CERTIFICATE TEMPLATE

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)

BOOKS AND REFERENCES:

• The Elements of Statistical Learning, by Trevor Hastie, Robert Tibshirani, Jerome H. Friedman (freely available online)
• Pattern Recognition and Machine Learning, by Christopher Bishop (optional)

2 reviews for Introduction to Machine Learning

biswajit – March 11, 2022

this is a very good course.

CHETHAN MS – October 14, 2022

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NPTEL Introduction to Machine Learning Assignment 2 Answers 2022

• by QuizXp Team
• January 30, 2022 February 22, 2022

Are you looking for the Answers to NPTEL Introduction to Machine Learning Assignment 2 – IIT Madras? This article will help you with the answer to the  Nation al Programme on Technology Enhanced Learning  ( NPTEL )  Course “ NPTEL Introduction to Machine Learning Assignment 2 “

What is 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.

CRITERIA TO GET A CERTIFICATE

Average assignment score = 25% of the 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 THE 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.

Below you can find the answers for NPTEL Introduction to Machine Learning Assignment 2

NPTEL Introduction to Machine Learning Assignment 2 Answers:-

Q1. Given a training data set of 10,000 instances, with each input instance having 17 dimensions and each output instance having 2 dimensions, the dimensions of the design matrix used in applying linear regression to this data is

(A) 10000 × 17 (B) 10002 × 17 (C) 10000 × 18 (D) 10000 × 19

Q2. Suppose we want to add a regularizer to the linear regression loss function, to control the magnitudes of the weights β . We have a choice between Ω1( β )=Σ pi =1| β | and Ω2( β )=Σ pi =1 β 2 Which one is more likely to result in sparse weights?

(A) Ω1 (B) Ω2 (C) Both Ω1 and Ω2 will result in sparse weights  (D) Neither of Ω1 or Ω2 can result in sparse weights

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Q3. The model obtained by applying linear regression on the identified subset of features may differ from the model obtained at the end of the process of identifying the subset during

(A) Forward stepwise selection (B) Backward stepwise selection (C) Forward stagewise selection (D) All of the above

Q4. Consider forward selection, backward selection and best subset selection with respect to the same data set. Which of the following is true?

(A) Best subset selection can be computationally more expensive than forward selection (B) Forward selection and backward selection always lead to the same result  (C) Best subset selection can be computationally less expensive than backward selection (D) Best subset selection and forward selection are computationally equally expensive

(E)Both (b) and (d)

???? Next Week Answers: Assignment 03 ????

Q5. In the lecture on Multivariate Regression, you learn about using orthogonalization iteratively to obtain regression co-effecients. This method is generally referred to as Multiple Regression using Successive Orthogonalization. In the formulation of the method, we observe that in iteration k , we regress the entire dataset on z 0, z 1,… zk −1 . It seems like a waste of computation to recompute the coefficients for z 0 a total of p times, z 1 a total of p −1 times and so on. Can we re-use the coefficients computed in iteration j for iteration j +1 for zj −1 ?

(A) No. Doing so will result in the wrong γ matrix. and hence, the wrong βi ’s.

(B) Yes. Since zj −1 is orthogonal to zj − l ∀ l ≤ j 1, the multiple regression in each iteration is essentially a univariate regression on each of the previous residuals. Since the regression coefficients for the previous residuals don’t change over iterations, we can re-use the coefficients for further iterations.

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Q6. Principal Component Regression (PCR) is an approach to find an orthogonal set of basis vectors which can then be used to reduce the dimension of the input. Which of the following matrices contains the principal component directions as its columns (follow notation from the lecture video)

Q7. Consider the following five training examples

We want to learn a function f ( x ) of the form f ( x )= a x + b which is parameterised by ( a , b ). Using squared error as the loss function, which of the following parameters would you use to model this function to get a solution with the minimum loss.

Q8. Here is a data set of words in two languages.

Let us build a nearest neighbours classifier that will predict which language a word belongs to. Say we represent each word using the following features.• Length of the word• Number of consonants in the word• Whether it ends with the letter ’o’ (1 if it does, 0 if it doesn’t)

For example, the representation of the word ‘waffle’ would be [6, 2, 0]. For a distance function, use the Manhattan distance.

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NPTEL Introduction to Machine Learning Assignment 2 Answers 2022:- All the Answers provided here to help the students as a reference, You must submit your assignment at your own knowledge.

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NPTEL: Exam Registration is open now for Jan 2023 courses!

Dear Candidate, Here is a golden opportunity for those who had previously enrolled in this course, but could not participate in the exams or were absent/did not pass the exam for this course. This course is being reoffered in Jan 2023 and we are giving you another chance to write the exam in April 2023 and obtain a certificate based on NPTEL norms. Do not let go of this unique opportunity to earn a certificate from the IITs/IISc.   IMPORTANT instructions for learners - Please read this carefully     1. The exam date for this course: April 30,2023 2. CLICK HERE to register for the exam. Please fill the exam form using the same Enrolled email id & make fee payment via the form, as before. 3.Choose from the Cities where the exam will be conducted:  Exam Cities 4. You DO NOT have to re-enroll in the courses if you have enough assignment scores in the previous immediate semester when the course was run to obtain a certificate.  5. You DO NOT have to resubmit Assignments in the previous semester if you have enough assignment scores in the previous immediate semester when the course was run to obtain a certificate.  6. If you do enroll to Jan 2023 course, we will take the best average assignment scores across this  semester & the immediate previous semester when the course was run.     NOTE:   Please check once if you have >= 40/100  in average assignment score that were conducted in this course to become eligible for the e-certificate, wherever applicable. If not, please submit assignments again in the Jan 2023 course to become eligible for the e-certificate. We will not be having new assignments in the previous semester's course. It is mandatory to enroll again in the Jan 2023 semester if you want to receive the fresh assignments of this current Jan 2023 semester. You can also submit assignments again if you want to take fresh assignments or need to improve your previous scores.   RECOMMENDATION: If you want to take new assignments or brush up on your lessons for the exam, please enroll in the Jan 2023 course.   LINK to enroll in the current course:    https://onlinecourses.nptel.ac.in/noc23_cs18/preview   4. Exam fees:    If you register for the exam and pay before Mar 17, 2023, 5:00 PM, Exam fees will be Rs. 1000/- per exam.   5. 50% fee waiver for the following categories:    Students belonging to the SC/ST category: please select Yes for the SC/ST option and upload the correct Community certificate.   Students belonging to the PwD category with more than 40% disability: please select Yes for the option and upload the relevant Disability certificate.      6. Last date for exam registration: Mar 17, 2023, 5:00 PM (Friday).     7. Mode of payment: Online payment - debit card/credit card/net banking/UPI.    8. 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Introduction to Machine Learning : Result Published!!

Dear Candidate, The exam scores and E Certificates have been released for October 2022 Exam(s). Step 1 - Are the results of my courses released? Please check the Results published courses list in the below links.:- October 2022 Exam - Click here Step 2 - How to check Results? Please login to internalapp.nptel.ac.in/ . and check your exam results. Use the same login credentials as used to register to the exam. What's next? Please read the pass criteria carefully and check against what you have gotten. If you still have any issues, please report the same here. internalapp.nptel.ac.in/ . We will reply within a week. Last date to report queries: 3 days within publishing of scores. Note : Hard copies of certificates will not be dispatched. The duration shown in the certificate will be based on the timeline of offering of the course in 2022, irrespective of which Assignment score that will be considered. Thanks and Best wishes. NPTEL Team

Introduction to Machine Learning : Final Feedback Form!!

Dear student, We are glad that you have attended the NPTEL online certification course. We hope you found the NPTEL Online course useful and have started using NPTEL extensively. In this regard, we would like to have feedback from you regarding our course and whether there are any improvements, you would like to suggest.   We are enclosing an online feedback form and would request you to spare some of your valuable time to input your observations. Your esteemed input will help us in serving you better. The link to give your feedback is: https://docs.google.com/forms/d/1kk_7r6xggVJ13gmU5keRHAMxE9GBlH7IpVtLV8kLXCk/viewform We thank you for your valuable time and feedback. Thanks & Regards, -NPTEL Team

October 2022 NPTEL Exams - Hall Tickets Released!

***THIS IS APPLICABLE ONLY FOR EXAM REGISTERED CANDIDATES*** Dear Candidate, Your Hall Ticket / admit card for the NPTEL Exam(s) in October, 2022 has been released. Please login to https://internalapp.nptel.ac.in/ using your exam registered email id and download your hall ticket. Note:  Requests for changes in exam city, exam center, exam date, session, or course will NOT be entertained. Please write to [email protected] for any further queries. All the best for your exams! Warm Regards NPTEL Team

Introduction to Machine Learning - Assignment 12 Solutions Released!!

Dear Learners, The Solutions of Week 12   for the course " Introduction to Machine Learning" have been released in the portal. Please go through the solution and in case of any doubt post your queries in the forum. Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=122&lesson=127 Happy Learning! Thanks & Regards, NPTEL Team

Introduction to Machine Learning : Problem solving Session Reminder!!

Dear learners, There will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Session 1: Date: October 18, 2022 - Tuesday Time: 06.00 PM - 07.00 PM Link to join:  https://meet.google.com/ngh-yrit-xhj Session 2: Date: October 18, 2022 - Tuesday Time: 07.00 PM - 08.00 PM Link to join:  https://meet.google.com/mjx-bxaa-etw Happy Learning. -NPTEL Team

Dear learners, There will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Session 1: Date: October 15, 2022 - Saturday Time: 03.00 PM - 04.00 PM Link to join:  meet.google.com/axm-qobw-xfs Session 2: Date: October 15, 2022 - Saturday Time: 04.00 PM - 05.00 PM Link to join:  meet.google.com/cqc-exiz-ehy Happy Learning. -NPTEL Team

Introduction to Machine Learning - Assignment 10 and 11 Solutions Released!!

Dear Learners, The Solutions of Week 10 and Week 11   for the course " Introduction to Machine Learning " have been released in the portal. Please go through the solution and in case of any doubt post your queries in the forum. Assignment 10 Solution Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=108&lesson=115 Assignment 11 Solution Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=116&lesson=121 Happy Learning! Thanks & Regards, NPTEL Team

Exam Format - Oct 29, 2022

Dear Candidate, ****This is applicable only for the exam registered candidates**** Type of exam will be available in the list: Click Here You will have to appear at the allotted exam center and produce your Hall ticket and Government Photo Identification Card (Example: Driving License, Passport, PAN card, Voter ID, Aadhaar-ID with your Name, date of birth, photograph and signature) for verification and take the exam in person.  You can find the final allotted exam center details in the hall ticket. The hall ticket is yet to be released.  We will notify the same through email and SMS. Type of exam: Computer based exam (Please check in the above list corresponding to your course name) The questions will be on the computer and the answers will have to be entered on the computer; type of questions may include multiple choice questions, fill in the blanks, essay-type answers, etc. Type of exam: Paper and pen Exam  (Please check in the above list corresponding to your course name) The questions will be on the computer. You will have to write your answers on sheets of paper and submit the answer sheets. Papers will be sent to the faculty for evaluation. On-Screen Calculator Demo Link: Kindly use the below link to get an idea of how the On-screen calculator will work during the exam. https://tcsion.com/ OnlineAssessment/ ScientificCalculator/ Calculator.html NOTE: Physical calculators are not allowed inside the exam hall. -NPTEL Team

Introduction to Machine Learning : Week 12 Feedback Form is live now !!

Dear Learner Thank you for continuing with the course and hope you are enjoying it. We would like to know if the expectations with which you joined this course are being met and hence please do take 2 minutes to fill out our weekly feedback form. It would help us tremendously in gauging the learner experience. Here is the link to the form: https://docs.google.com/forms/d/11GWqQ4vAjllEU2XkMjZqg3xnEGTk_ssnvpnK8ms_9fU/viewform Thank you. -NPTEL team

Dear learners, There will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Session 1: Date: October 11, 2022 - Tuesday Time: 06.00 PM - 07.00 PM Link to join:  https://meet.google.com/ngh-yrit-xhj Session 2: Date: October 11, 2022 - Tuesday Time: 07.00 PM - 08.00 PM Link to join:  https://meet.google.com/mjx-bxaa-etw Happy Learning. -NPTEL Team

Introduction to Machine Learning : Week 12 content is live now !!

Dear Learners, The lecture videos for Week 12 have been uploaded for the course " Introduction to Machine Learning ". The lectures can be accessed using the following link:   Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=122&lesson=123 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Practice Assignment-12 for Week 12 is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=122&assessment=145 Assignment-12 for Week 12  is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=122&assessment=176 The assignment has to be submitted on or before Wednesday,[19/10/2022], 23:59 IST. As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards, -NPTEL Team

Dear learners, There will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Session 1: Date: October 08, 2022 - Saturday Time: 03.00 PM - 04.00 PM Link to join:  meet.google.com/axm-qobw-xfs Session 2: Date: October 08, 2022 - Saturday Time: 04.00 PM - 05.00 PM Link to join:  meet.google.com/cqc-exiz-ehy Happy Learning. -NPTEL Team

Introduction to Machine Learning : Week 11 Feedback Form is live now !!

Dear learners, There will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Session 1: Date: October 04, 2022 - Tuesday Time: 06.00 PM - 07.00 PM Link to join:  https://meet.google.com/ngh-yrit-xhj Session 2: Date: October 04, 2022 - Tuesday Time: 07.00 PM - 08.00 PM Link to join:  https://meet.google.com/mjx-bxaa-etw Happy Learning. -NPTEL Team

Dear learners, There will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Session 1: Date: October 01, 2022 - Saturday Time: 03.00 PM - 04.00 PM Link to join:  meet.google.com/axm-qobw-xfs Session 2: Date: October 01, 2022 - Saturday Time: 04.00 PM - 05.00 PM Link to join:  meet.google.com/cqc-exiz-ehy Happy Learning. -NPTEL Team

Introduction to Machine Learning : Week 11 content is live now !!

Dear Learners, The lecture videos for Week 11 have been uploaded for the course " Introduction to Machine Learning ". The lectures can be accessed using the following link:   Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=116&lesson=117 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Practice Assignment-11 for Week 11 is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=116&assessment=144 Assignment-11 for Week 11 is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=116&assessment=175 The assignment has to be submitted on or before Wednesday,[12/10/2022], 23:59 IST. As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards, -NPTEL Team

Introduction to Machine Learning - Assignment 7 to 9 Solutions Released!!

Dear Learners, The Solutions of Week 7 to Week 9   for the course " Introduction to Machine Learning " have been released in the portal. Please go through the solution and in case of any doubt post your queries in the forum. Assignment 7 Solution Link:   https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=82&lesson=91 Assignment 8 Solution Link:   https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=92&lesson=99 Assignment 9 Solution Link:   https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=100&lesson=106 Happy Learning! Thanks & Regards, NPTEL Team

Dear learners, There will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Session 1: Date: September 27, 2022 - Tuesday Time: 06.00 PM - 07.00 PM Link to join:  https://meet.google.com/ngh-yrit-xhj Session 2: Date: September 27, 2022 - Tuesday Time: 07.00 PM - 08.00 PM Link to join:  https://meet.google.com/mjx-bxaa-etw Happy Learning. -NPTEL Team

Introduction to Machine Learning : Week 10 Feedback Form is live now !!

Introduction to machine learning : week 10 content is live now .

Dear Learners, The lecture videos for Week 10  have been uploaded for the course " Introduction to Machine Learning ". The lectures can be accessed using the following link:   Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=108&lesson=109 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Practice Assignment-10 for Week 10 is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=108&assessment=143 Assignment-10 for Week 10  is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=108&assessment=174 The assignment has to be submitted on or before Wednesday,[05/10/2022], 23:59 IST. As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards, -NPTEL Team

Dear learners, There will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Session 1: Date: September 24, 2022 - Saturday Time: 03.00 PM - 04.00 PM Link to join:  meet.google.com/axm-qobw-xfs Session 2: Date: September 24, 2022 - Saturday Time: 04.00 PM - 05.00 PM Link to join:  meet.google.com/cqc-exiz-ehy Happy Learning. -NPTEL Team

Dear learners, There will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Session 1: Date: September 20, 2022 - Tuesday Time: 06.00 PM - 07.00 PM Link to join:  https://meet.google.com/ngh-yrit-xhj Session 2: Date: September 20, 2022 - Tuesday Time: 07.00 PM - 08.00 PM Link to join:  https://meet.google.com/mjx-bxaa-etw Happy Learning. -NPTEL Team

Introduction to Machine Learning : Week 9 Feedback Form is live now !!

Introduction to machine learning : week 9 content is live now .

Dear Learners, The lecture videos for Week 9  have been uploaded for the course " Introduction to Machine Learning ". The lectures can be accessed using the following link:   Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=100&lesson=101 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Practice Assignment-9 for Week 9 is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=100&assessment=142 Assignment-9 for Week 9  is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=100&assessment=173 The assignment has to be submitted on or before Wednesday,[28/09/2022], 23:59 IST. As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards, -NPTEL Team

Dear learners, There will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Date: September 18, 2022 - Sunday Time: 03.00 PM - 04.00 PM Link to join:  meet.google.com/axm-qobw-xfs Happy Learning. -NPTEL Team

Dear learners, There will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Date: September 17, 2022 - Saturday Time: 04.00 PM - 05.00 PM Link to join:  meet.google.com/cqc-exiz-ehy Happy Learning. -NPTEL Team

Dear learners, There will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Session 1: Date: September 13, 2022 - Tuesday Time: 06.00 PM - 07.00 PM Link to join:  https://meet.google.com/ngh-yrit-xhj Session 2: Date: September 13, 2022 - Tuesday Time: 07.00 PM - 08.00 PM Link to join:  https://meet.google.com/mjx-bxaa-etw Happy Learning. -NPTEL Team

Introduction to Machine Learning : Week 8 Feedback Form is live now !!

Survey on problem solving sessions - introduction to machine learning( noc22-cs73 ).

Dear Learners, We would like to know if the expectations with which you attended this problem solving session are being met and hence please do take 2 minutes to fill out our feedback form. It would help us tremendously in gauging the learner experience. Here is the link to the form: https://docs.google.com/forms/d/1IQBEYiNLB9LV8ch7KbSWTiZ3-EyXu3zAEwM1YuYY7aw/viewform - NPTEL Team

Introduction to Machine Learning : Week 8 content is live now !!

Dear Learners, The lecture videos for Week 8  have been uploaded for the course " Introduction to Machine Learning ". The lectures can be accessed using the following link:   Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=92&lesson=93 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Practice Assignment-8 for Week 8 is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=92&assessment=141 Assignment-8 for Week 8  is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=92&assessment=172 The assignment has to be submitted on or before Wednesday,[21/09/2022], 23:59 IST. As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards, -NPTEL Team

Dear learners, There will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Session 1: Date: September 10, 2022 - Saturday Time: 03.00 PM - 04.00 PM Link to join:  meet.google.com/axm-qobw-xfs Session 2: Date: September 10, 2022 - Saturday Time: 04.00 PM - 05.00 PM Link to join:  meet.google.com/cqc-exiz-ehy Happy Learning. -NPTEL Team

Introduction to Machine Learning - Assignment 6 Solutions Released!!

Dear Learners, The Solutions of Week 6   for the course " Introduction to Machine Learning6Solution Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=70&lesson=81 Happy Learning! Thanks & Regards, NPTEL Team

Introduction to Machine Learning : Week 7 Feedback Form is live now !!

Dear learners, There will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Session 1: Date: September 06, 2022 - Tuesday Time: 06.00 PM - 07.00 PM Link to join:  https://meet.google.com/ngh-yrit-xhj Session 2: Date: September 06, 2022 - Tuesday Time: 07.00 PM - 08.00 PM Link to join:  https://meet.google.com/mjx-bxaa-etw Happy Learning. -NPTEL Team

Introduction to Machine Learning : Week 7 content is live now !!

Dear Learners, The lecture videos for Week 7 have been uploaded for the course " Introduction to Machine Learning ". The lectures can be accessed using the following link:   Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=82&lesson=83 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Practice Assignment-7 for Week 7 is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=82&assessment=140 Assignment-7 for Week 7  is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=82&assessment=169 The assignment has to be submitted on or before Wednesday,[14/09/2022], 23:59 IST. As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards, -NPTEL Team

Dear learners, There will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Session 1: Date: September 03, 2022 - Saturday Time: 03.00 PM - 04.00 PM Link to join:  meet.google.com/axm-qobw-xfs Session 2: Date: September 03, 2022 - Saturday Time: 04.00 PM - 05.00 PM Link to join:  meet.google.com/cqc-exiz-ehy Happy Learning. -NPTEL Team

Introduction to Machine Learning : Week 6 content is live now !!

Dear Learners, The lecture videos for Week 6 have been uploaded for the course " Introduction to Machine Learning ". The lectures can be accessed using the following link:   Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=70&lesson=71 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Practice Assignment-6 for Week 6 is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=70&assessment=139 Assignment-6 for Week 6 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=70&assessment=168 The assignment has to be submitted on or before Wednesday,[07/09/2022], 23:59 IST. As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards, -NPTEL Team

Introduction to Machine Learning : Week 6 Feedback Form is live now !!

Dear learners, There will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Session 1: Date: August 30, 2022 - Tuesday Time: 06.00 PM - 07.00 PM Link to join:  https://meet.google.com/ngh-yrit-xhj Session 2: Date: August 30, 2022 - Tuesday Time: 07.00 PM - 08.00 PM Link to join:  https://meet.google.com/mjx-bxaa-etw Happy Learning. -NPTEL Team

Dear learners, There will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Session 1: Date: August 27, 2022 - Saturday Time: 03.00 PM - 04.00 PM Link to join:  meet.google.com/axm-qobw-xfs Session 2: Date: August 27, 2022 - Saturday Time: 04.00 PM - 05.00 PM Link to join:  meet.google.com/cqc-exiz-ehy Happy Learning. -NPTEL Team

Dear learners, There will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Session 1: Date: August 23, 2022 - Tuesday Time: 06.00 PM - 07.00 PM Link to join:  https://meet.google.com/ngh-yrit-xhj Session 2: Date: August 23, 2022 - Tuesday Time: 07.00 PM - 08.00 PM Link to join:  https://meet.google.com/mjx-bxaa-etw Happy Learning. -NPTEL Team

Introduction to Machine Learning : Week 5 Feedback Form is live now !!

Dear learners, There will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Session 1: Date: August 20, 2022 - Saturday Time: 03.00 PM - 04.00 PM Link to join:  meet.google.com/axm-qobw-xfs Session 2: Date: August 20, 2022 - Saturday Time: 04.00 PM - 05.00 PM Link to join:  meet.google.com/cqc-exiz-ehy Happy Learning. -NPTEL Team

Introduction to Machine Learning : Week 5 content is live now !!

Dear Learners, The lecture videos for Week 5 have been uploaded for the course " Introduction to Machine Learning ". The lectures can be accessed using the following link:   Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=60&lesson=61 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Practice Assignment-5 for Week 5 is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=60&assessment=138 Assignment-5 for Week 5 is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=60&assessment=167 The assignment has to be submitted on or before Wednesday,[31/08/2022], 23:59 IST. As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards, -NPTEL Team

Introduction to Machine Learning : Week 4 Feedback Form is live now !!

Dear learners, There will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Session 1: Date: August 16, 2022 - Tuesday Time: 06.00 PM - 07.00 PM Link to join:  https://meet.google.com/ngh-yrit-xhj Session 2: Date: August 16, 2022 - Tuesday Time: 07.00 PM - 08.00 PM Link to join:  https://meet.google.com/mjx-bxaa-etw Happy Learning. -NPTEL Team

Dear learners, There will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Date: August 13, 2022-Saturday Time: 04.00 PM - 05.00 PM Link to join:  meet.google.com/cqc-exiz-ehy Happy Learning. -NPTEL Team

Dear learners, There will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Date: August 13, 2022-Saturday Time: 03.00 PM - 04.00 PM Link to join:  meet.google.com/axm-qobw-xfs Happy Learning. -NPTEL Team

Introduction to Machine Learning : Week 4 Assignment Correction!

Dear Learners, Find the following updates in questions for the Week 4 assignment: For Q6,7: Kindly download the modified version of Iris dataset from this link. Available at: ( https://goo.gl/vchhsd ) The dataset contains 150 points, and each input point has 4 features and belongs to one among three classes. Use the first 100 points as the training data and the remaining 50 as test data. In the following questions, to report accuracy, use the test dataset. You can round off the accuracy value to the nearest 2-decimal point number. ( Note: Do not change the order of data points.) Note: If you have already submitted the assignment then please recheck the assignment and submit the answers again. -NPTEL Team.

Dear learners, There will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Session 1: Date: August 13, 2022 - Saturday Time: 03.00 PM - 04.00 PM Link to join:  meet.google.com/axm-qobw-xfs Session 2: Date: August 13, 2022 - Saturday Time: 04.00 PM - 05.00 PM Link to join:  meet.google.com/cqc-exiz-ehy Happy Learning. -NPTEL Team

Introduction to Machine Learning : Week 4 content is live now !!

Dear Learners, The lecture videos for Week 4 have been uploaded for the course " Introduction to Machine Learning ". The lectures can be accessed using the following link:   Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=51&lesson=52 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Practice Assignment-4 for Week 4 is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=51&assessment=146 Assignment-4 for Week 4 is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=51&assessment=166 The assignment has to be submitted on or before Wednesday,[24/08/2022], 23:59 IST. As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards, -NPTEL Team

Introduction to Machine Learning : Week 3 Feedback Form is live now !!

Dear learners, There will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Session 1: Date: August 9, 2022 - Tuesday Time: 06.00 PM - 07.00 PM Link to join:  https://meet.google.com/ngh-yrit-xhj Session 2: Date: August 9, 2022 - Tuesday Time: 07.00 PM - 08.00 PM Link to join:  https://meet.google.com/mjx-bxaa-etw Happy Learning. -NPTEL Team

Introduction to Machine Learning: Reminder for Assignment 1 & 2 deadline!!

Dear Learners, The Deadline for Assignments 1 & 2 will close on Wednesday,[10/08/2022], 23:59 IST. Kindly submit the assignments before the deadline. Thanks and Regards, -NPTEL Team

Dear learners, There will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Date: August 6, 2022-Saturday Time: 04.00 PM - 05.00 PM Link to join:  meet.google.com/cqc-exiz-ehy Happy Learning. -NPTEL Team

Dear learners, There will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Date: August 6, 2022-Saturday Time: 03.00 PM - 04.00 PM Link to join:  meet.google.com/axm-qobw-xfs Happy Learning. -NPTEL Team

Introduction to Machine Learning : Week 3 content is live now !!

Dear Learners, The lecture videos for Week 3 have been uploaded for the course " Introduction to Machine Learning ". The lectures can be accessed using the following link:   Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=42&lesson=43 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Practice Assignment-3 for Week 3 is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=42&assessment=147 Assignment-3 for Week 3 is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=42&assessment=165 The assignment has to be submitted on or before Wednesday,[17/08/2022], 23:59 IST. As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards, -NPTEL Team

Introduction to Machine Learning : Week 2 Feedback Form is live now !!

Introduction to machine learning : problem solving session.

Dear Learner, We have uploaded the Recorded videos of the Problem Solving Session of Week 1 . Videos are uploaded inside the Separate Unit called " Problem Solving Session " along with the slides used wherever applicable. Login to the course on swayam.gov.in to check the same. -NPTEL Team

Dear learners, There will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Date: August 2, 2022 - Tuesday Time: 06.00 PM - 07.00 PM Link to join:  https://meet.google.com/ngh-yrit-xhj Happy Learning. -NPTEL Team

Dear learners, There will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Date: August 2, 2022 - Tuesday Time: 07.00 PM - 08.00 PM Link to join:  https://meet.google.com/mjx-bxaa-etw Happy Learning. -NPTEL Team

Dear learners, There will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Date: July 30, 2022-Saturday Time: 03.00 PM - 04.00 PM Link to join:  meet.google.com/axm-qobw-xfs Happy Learning. -NPTEL Team

Dear learners, There will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Date: July 30, 2022-Saturday Time: 04.00 PM - 05.00 PM Link to join:  meet.google.com/cqc-exiz-ehy Happy Learning. -NPTEL Team

Introduction to Machine Learning : Problem solving Session

Dear learner, Every week there will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that  will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Start Date:   July 30, 2022 When: EverySaturday Time: 03.00 PM - 04.00 PM Link to join:   meet.google.com/axm-qobw-xfs Thank you. -NPTEL team

Dear learner, Every week there will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that  will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Start Date:   August 2, 2022 When: EveryTuesday Time: 06.00 PM - 07.00 PM Link to join:   https://meet.google.com/ngh-yrit-xhj Thank you. -NPTEL team

Dear learner, Every week there will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that  will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Start Date:   August 2, 2022 When: EveryTuesday Time: 07.00 PM - 08.00 PM Link to join:   https://meet.google.com/mjx-bxaa-etw Thank you. -NPTEL team

Dear learner, Every week there will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that  will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Start Date:   July 30, 2022 When: EverySaturday Time: 04.00 PM - 05.00 PM Link to join:   meet.google.com/cqc-exiz-ehy Thank you. -NPTEL team

Introduction to Machine Learning : Week 1 Feedback Form

Introduction to machine learning : week 1 content is live now .

Dear Learners, The lecture videos for Week 1 have been uploaded for the course " Introduction to Machine Learning ". The lectures can be accessed using the following link:   Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=22&lesson=23 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Practice Assignment-1 for Week 1 is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=22&assessment=136 Assignment-1 for Week 1 is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=22&assessment=160 The assignment has to be submitted on or before Wednesday,[10/08/2022], 23:59 IST. As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards, -NPTEL Team

Introduction to Machine Learning : Assignment 0 is live now!!

Dear Learners, We welcome you all to the course " Introduction to Machine Learning " . The assignment 0 has been released. This assignment is based on a prerequisite of the course. You can find the assignment in the link :  https://onlinecourses.nptel.ac.in/noc22_cs73/unit?unit=16&assessment=135 Please note that this assignment is only for practice and it will not be graded. Thanks & Regards   -NPTEL Team

Introduction to Machine Learning - Week-1 video is live now !!

Dear Learners,   The lecture videos for Week 1 have been uploaded for the course Introduction to Machine Learning . The lectures can be accessed using the following link:   Link:   https://onlinecourses.nptel.ac.in/noc22_ee121/unit?unit=16&lesson=45   The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already).   Assignment will be released shortly.   As we have done so far, please use the discussion forums if you have any questions on this module.   Thanks and Regards,   -NPTEL Team

NPTEL: Exam Registration is open now for July 2022 courses!

Dear Learner, 

Here is the much-awaited announcement on registering for the July 2022 NPTEL course certification exam. 

1. The registration for the certification exam is open only to those learners who have enrolled in the course. 

2. If you want to register for the exam for this course, login here using the same email id which you had used to enroll to the course in Swayam portal. Please note that Assignments submitted through the exam registered email id ALONE will be taken into consideration towards final consolidated score & certification. 

3 . Date of exam: Oct 29, 2022

CLICK HERE to register for the exam. 

Choose from the Cities where exam will be conducted: Exam Cities

4. Exam fees: 

If you register for the exam and pay before Sep 12, 2022, 10:00 AM, Exam fees will be Rs. 1000/- per exam . 

If you register for exam before Sep 12, 2022, 10:00 AM and have not paid or if you register between Sep 12, 2022, 10:00 AM & Sep 16, 2022, 10:00 AM, Exam fees will be Rs. 1500/- per exam 

5. 50% fee waiver for the following categories: 

Students belonging to the SC/ST category: please select Yes for the SC/ST option and upload the correct Community certificate.

Students belonging to the PwD category with more than 40% disability: please select Yes for the option and upload the relevant Disability certificate. 

6. Last date for exam registration: Sep 16, 2022, 10:00 AM (Friday). 

7. Mode of payment: Online payment - debit card/credit card/net banking/UPI. 

8. HALL TICKET: 

The hall ticket will be available for download tentatively by 2 weeks prior to the exam date . We will confirm the same through an announcement once it is published. 

9. FOR CANDIDATES WHO WOULD LIKE TO WRITE MORE THAN 1 COURSE EXAM:- you can add or delete courses and pay separately – till the date when the exam form closes. Same day of exam – you can write exams for 2 courses in the 2 sessions. Same exam center will be allocated for both the sessions. 

10. Data changes: 

Last date for data changes: Sep 16, 2022, 10:00 AM :  

All the fields in the Exam form except for the following ones can be changed until the form closes. 

The following 6 fields can be changed ONLY when there are NO courses in the course cart. And you will be able to edit the following fields only if you: - 

REMOVE unpaid courses from the cart And/or - CANCEL paid courses 

1. Do you come under the SC/ST category? * 

2. SC/ST Proof 

3. Are you a person with disabilities? * 

4. Are you a person with disabilities above 40%? 

5. Disabilities Proof 

6. What is your role ? 

Note: Once you remove or cancel a course, you will be able to edit these fields immediately. 

But, for cancelled courses, refund of fees will be initiated only after 2 weeks. 

11. LAST DATE FOR CANCELLING EXAMS and getting a refund: Sep 16, 2022, 10:00 AM  

12. Click here to view Timeline and Guideline : Guideline

Domain Certification

Domain Certification helps learners to gain expertise in a specific Area/Domain. This can be helpful for learners who wish to work in a particular area as part of their job or research or for those appearing for some competitive exam or becoming job ready or specialising in an area of study.  

Every domain will comprise Core courses and Elective courses. Once a learner completes the requisite courses per the mentioned criteria, you will receive a Domain Certificate showcasing your scores and the domain of expertise. Kindly refer to the following link for the list of courses available under each domain:  https://nptel.ac.in/domains

Outside India Candidates

Candidates who are residing outside India may also fill the exam form and pay the fees. Mode of exam and other details will be communicated to you separately.

Thanks & Regards, 

Introduction to Machine Learning: Welcome to NPTEL Online Course - July 2022!!

• Every week, about 2.5 to 4 hours of videos containing content by the Course instructor will be released along with an assignment based on this. Please watch the lectures, follow the course regularly and submit all assessments and assignments before the due date. Your regular participation is vital for learning and doing well in the course. This will be done week on week through the duration of the course.
• Please do the assignments yourself and even if you take help, kindly try to learn from it. These assignments will help you prepare for the final exams. Plagiarism and violating the Honor Code will be taken very seriously if detected during the submission of assignments.
• The announcement group - will only have messages from course instructors and teaching assistants - regarding the lessons, assignments, exam registration, hall tickets, etc.
• The discussion forum (Ask a question tab on the portal) - is for everyone to ask questions and interact. Anyone who knows the answers can reply to anyone's post and the course instructor/TA will also respond to your queries.
• Please make maximum use of this feature as this will help you learn much better.
• If you have any questions regarding the exam, registration, hall tickets, results, queries related to the technical content in the lectures, any doubts in the assignments, etc can be posted in the forum section
• The course is free to enroll and learn from. But if you want a certificate, you have to register and write the proctored exam conducted by us in person at any of the designated exam centres.
• The exam is optional for a fee of Rs 1000/- (Rupees one thousand only).
• Date and Time of Exams: October 29, 2022 Morning session 9am to 12 noon; Afternoon Session 2 pm to 5 pm.
• Registration URL: Announcements will be made when the registration form is open for registrations.
• The online registration form has to be filled and the certification exam fee needs to be paid. More details will be made available when the exam registration form is published. If there are any changes, it will be mentioned then.
• Please check the form for more details on the cities where the exams will be held, the conditions you agree to when you fill the form etc.
• Once again, thanks for your interest in our online courses and certification. Happy learning.

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NPTEL Introduction to Machine Learning Assignment 1 Answers 2023

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 Week 1 Assignment Answer 2023

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 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.

2. 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.

3. 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.

4. 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.

5. 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 p e rson
• 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.

6. 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

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

• None of the above

9. 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.

10. 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

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.

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.

Answers will be Uploaded Shortly and it will be Notified on Telegram, So  JOIN NOW

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.

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.

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)?

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

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

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

👇 For Week 02 Assignment Answers 👇

9. Bias and variance are given by:

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

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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.

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

NOTE:- Answers of  Introduction to Machine Learning Assignment 1 will be uploaded shortly and it will be notified on Telegram, So  JOIN NOW

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

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

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.

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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