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

Dear Candidate,

Here is a golden opportunity for those who had previously enrolled in this course during the Jan 2023 semester, but could not participate in the exams or were absent/did not pass the exam for this course. This course is being reoffered in July 2023 and we are giving you another chance to write the exam in October 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: Oct 29, 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 exam will be conducted: Exam Cities

4. You DO NOT have to re-enroll in the courses. 

5. You DO NOT have to resubmit Assignments OR participate in the non-proctored programming exams(if applicable) in the previous semester

6. If you do enroll in the July 2023 course, we will take the best average assignment scores/non-proctored programming exam(if applicable) score across the two semesters.

Please check once if you have >= 40/100  in average assignment score and also participated and satisfied the criteria in the non-proctored programming exams(if applicable) that were conducted in Jan 2023 to become eligible for the e-certificate, wherever applicable.

If not, please submit assignments again in the July 2023 course and also participate in the non-proctored programming exams(if applicable) to become eligible for the e-certificate.

We will not be having new assignments or unproctored exams(if applicable) in the previous semester's (Jan 2023) course. 

RECOMMENDATION: If you want to take new assignments and an unproctored exam(if applicable) or brush up on your lessons for the exam, please enroll in the July 2023 course.

Click here to enroll in the current course, links are provided corresponding to the course name.

7. Exam fees: 

If you register for the exam and pay before Aug 14, 2023, 5:00 PM, Exam fees will be Rs. 1000/- per exam .

8. 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. 

9. Last date for exam registration: Aug 18, 2023, 5:00 PM (Friday). 

10. Between Aug 14, 2023, 5:00 PM & Aug 18, 2023, 5:00 PM late fee will be applicable.

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

12. 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. 

13. 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. 

14. Data changes: 

Last date for data changes: Aug 18, 2023, 5:00 PM :  

We will charge an additional fee of Rs. 200 to make any changes related to name, DOB, photo, signature, SC/ST and PWD certificates after the last date of data changes.

The following 6 fields can be changed (until the form closes) ONLY when there are NO courses in the course cart. And you will be able to edit those 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. 

15. LAST DATE FOR CANCELLING EXAMS and getting a refund: Aug 18, 2023, 5:00 PM  

16. 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, 

Thank you for learning with NPTEL!!

Dear Learner, Thank you for taking the course with NPTEL!! Hope you enjoyed the journey with us. The results for this course have been published and we are closing this course now.  You will still have access to the contents and assignments of this course, if you click on the course name from the "Mycourses" tab on swayam.gov.in. For any further queries please write to [email protected] . - Team NPTEL

Introduction to Machine Learning : Result Published!!

                                      ***THIS IS APPLICABLE ONLY FOR EXAM REGISTERED CANDIDATES***                             ****Please don't click on below link, if you are not registered/not present for the Exam****                          Dear Candidate, The exam scores and E Certificates have been released for April 2023 Exam(s). Step 1 - Are the results of my courses released? Please check the Results published courses list in the below links.:- Apr 2023 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 2023, irrespective of which Assignment score that will be considered. Thanks and Best wishes. NPTEL Team

Survey on Problem Solving sessions - Introduction to Machine Learning - (noc23-cs18)

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/18R3vlAYxYGwda5bbutnUq0R2MSgiMRrbve5cJ7qD5W8/viewform -NPTEL TEAM

Introduction to Machine Learning : Final Feedback Form !!!

Dear students, 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/1cD0v9YXupkwZMligiNMvxoczy5-iYj21rpSjONuAX54/viewform We thank you for your valuable time and feedback. Thanks & Regards, -NPTEL Team

April 2023 NPTEL Exams - Hall Tickets Released!

***THIS IS APPLICABLE ONLY FOR EXAM REGISTERED CANDIDATES***     ****Please don't click on below link, if you are not registered for the Exam**** Dear Candidate, Your Hall Ticket / admit card for the NPTEL Exam(s) in April, 2023 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 solutions for Assignment 12 live now!!

Dear Learner, Assignment 12 solutions are available in the portal, Go through it once . Assignment 12 Solution Link :  https://onlinecourses.nptel.ac.in/noc23_cs18/unit?unit=129&lesson=134 -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:April 22, 2023 - Saturday Time:03.00 PM - 05.00 PM Link to join: https://meet.google.com/ajc-ehpn-eey Session 2 :  Date: April 22, 2023 - Saturday Time:07.00 PM - 09.00 PM Link to join: https://meet.google.com/aqg-nqgx-mgj 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: April 21, 2023 - Friday Time:06.00 PM - 08.00 PM Link to join: https://meet.google.com/chq-qqbr-fxd 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:April 15, 2023 - Saturday Time:03.00 PM - 05.00 PM Link to join: https://meet.google.com/ajc-ehpn-eey Session 2 :  Date: April 15, 2023 - Saturday Time:07.00 PM - 09.00 PM Link to join: https://meet.google.com/aqg-nqgx-mgj Happy Learning. -NPTEL Team

Introduction to Machine Learning : Problem solving Session Updated !!

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: April 14, 2023 - Friday Time: 04.00 PM - 06.00 PM Link to join: https://meet.google.com/chq-qqbr-fxd Happy Learning. -NPTEL Team

Introduction to Machine Learning : Assignment solutions for Assignment 11 live now!!

Dear Learner, Assignment 11 solutions are available in the portal, Go through it once . Assignment 11 Solution Link :  https://onlinecourses.nptel.ac.in/noc23_cs18/unit?unit=122&lesson=127 -Nptel Team

Week 12 Feedback Form: Introduction to Machine Learning

Dear Learners, 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/12xV5aJuc8r4uCZJVncM-uW6WVEMjFfIw6aSDN58uMbw/viewform Thank you -NPTEL team

Exam Format - April, 2023 !!

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. 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:April 08, 2023 - Saturday Time:03.00 PM - 05.00 PM Link to join: https://meet.google.com/ajc-ehpn-eey Session 2 :  Date: April 08, 2023 - Saturday Time:07.00 PM - 09.00 PM Link to join: https://meet.google.com/aqg-nqgx-mgj 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/noc23_cs18/unit?unit=129&lesson=130 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/noc23_cs18/unit?unit=129&assessment=171 Assignment-12  for Week-12  is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc23_cs18/unit?unit=129&assessment=159 The assignment has to be submitted on or before Wednesday,[19/04/2023], 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: April 07, 2023 - Friday Time:06.00 PM - 08.00 PM Link to join: https://meet.google.com/chq-qqbr-fxd Happy Learning. -NPTEL Team

Introduction to Machine Learning : Assignment solutions for Assignment 10 live now!!

Dear Learner, Assignment 10 solutions are available in the portal, Go through it once . Assignment 10 Solution Link :  https://onlinecourses.nptel.ac.in/noc23_cs18/unit?unit=113&lesson=120 -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:April 01, 2023 - Saturday Time:03.00 PM - 05.00 PM Link to join: https://meet.google.com/ajc-ehpn-eey Session 2 :  Date: April 01, 2023 - Saturday Time:07.00 PM - 09.00 PM Link to join: https://meet.google.com/aqg-nqgx-mgj 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/noc23_cs18/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-11  for Week-11  is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc23_cs18/unit?unit=122&assessment=170 Assignment-11  for Week-11  is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc23_cs18/unit?unit=122&assessment=158 The assignment has to be submitted on or before Wednesday,[12/04/2023], 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 solutions for Assignment 9 live now!!

Dear Learner, Assignment 9 solutions are available in the portal, Go through it once . Assignment 9 Solution Link :  https://onlinecourses.nptel.ac.in/noc23_cs18/unit?unit=104&lesson=110 -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: March 31, 2023 - Friday Time:06.00 PM - 08.00 PM Link to join: https://meet.google.com/chq-qqbr-fxd Happy Learning. -NPTEL Team

Week 10 Feedback Form: Introduction to Machine Learning

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:March 25, 2023 - Saturday Time:03.00 PM - 05.00 PM Link to join: https://meet.google.com/ajc-ehpn-eey Session 2 :  Date: March 25, 2023 - Saturday Time:07.00 PM - 09.00 PM Link to join: https://meet.google.com/aqg-nqgx-mgj Happy Learning. -NPTEL Team

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/noc23_cs18/unit?unit=113&lesson=114 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/noc23_cs18/unit?unit=113&assessment=169 Assignment-10  for Week-10  is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc23_cs18/unit?unit=113&assessment=157 The assignment has to be submitted on or before Wednesday,[05/04/2023], 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 : Problem solving Session Postponed!!

Dear learner, Due to unavoidable circumstances, The Problem solving Session organized today (March 24, 2023 - Friday)(06.00 PM - 08.00 PM) is Postponed to March 25, 2023 - Saturday. We invite you to join the session and get your doubts cleared and learn better. Date: March 25, 2023 - Saturday Time:  03.00 PM - 05.00 PM Link to join:  https://meet.google.com/chq-qqbr-fxd Happy Learning. -NPTEL Team

Introduction to Machine Learning : Assignment solutions for Assignment 8 live now!!

Dear Learner, Assignment 8 solutions are available in the portal, Go through it once . Assignment 8 Solution Link :  https://onlinecourses.nptel.ac.in/noc23_cs18/unit?unit=95&lesson=102 -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: March 24, 2023 - Friday Time:06.00 PM - 08.00 PM Link to join: https://meet.google.com/chq-qqbr-fxd Happy Learning. -NPTEL Team

Introduction to Machine Learning : Problem solving Session Cancellation!!

Dear learner, Due to unavoidable circumstances, The Problem solving Session organized for the course Introduction to Machine Learning on every  Saturday  from  05.00 PM - 07.00 PM  is cancelled. -NPTEL Team

Potential additional date (April 28th) for the April 2023 NPTEL exams

***THIS IS APPLICABLE ONLY FOR EXAM REGISTERED CANDIDATES*** Dear Student, Greetings from NPTEL! The Jan 2023 session is coming to an end soon and it’s time to put all your best efforts for the certification exam. The NPTEL team has always been there to make your learning process a joyful one and we hope that with your support we will be able to overcome the challenges of conducting a nation-wide exam of this magnitude. With the closure of exam registration form for the NPTEL April 2023 exams, the final registration count stands at 5.1 Lakh compared to 3.7 Lakh reported during the Jul-Dec 2022 semester. Based on the previous semester data, seats at the certification exam centres are booked by NPTEL at the beginning of the exam registration process. As the semester progresses, sometimes these numbers exceed our estimate, especially on certain dates and certain exam cities. Our goal is to allocate the chosen exam cities to all our learners. And by and large, we are able to allocate the chosen cities and dates with active support from our exam partner and our partner colleges. All efforts are being made to allocate the city of choice or the next nearest exam city for the April 29th/30th 2023 exams, as scheduled. However, in view of the unexpectedly large volume of exam registrations & limitation of seats at certain cities on a particular date, we may be compelled to shift the exam date of certain candidates to 28th April 2023 (Friday) as a last resort, only after exhausting all possibilities. We hope that you will appreciate our logistical constraints of such rescheduling and extend all necessary support as before and participate in the exam. With best wishes for the forthcoming exam(s), Warm regards, NPTEL Team

Introduction to Machine Learning : Assignment solutions for Assignment 7 live now!!

Dear Learner, Assignment 7 solutions are available in the portal, Go through it once . Assignment 7 Solution Link :  https://onlinecourses.nptel.ac.in/noc23_cs18/unit?unit=84&lesson=93 -Nptel Team

Week 9 Feedback Form: Introduction to Machine Learning

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:March 18, 2023 - Saturday Time:03.00 PM - 05.00 PM Link to join: https://meet.google.com/ajc-ehpn-eey Session 2 :  Date: March 18, 2023 - Saturday Time:05.00 PM - 07.00 PM Link to join: https://meet.google.com/epj-ejva-hfv Session 3 :  Date: March 18, 2023 - Saturday Time:07.00 PM - 09.00 PM Link to join: https://meet.google.com/aqg-nqgx-mgj Happy Learning. -NPTEL Team

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/noc23_cs18/unit?unit=104&lesson=105 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/noc23_cs18/unit?unit=104&assessment=168 Assignment-9  for Week-9  is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc23_cs18/unit?unit=104&assessment=156 The assignment has to be submitted on or before Wednesday,[29/03/2023], 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: March 17, 2023 - Friday Time:06.00 PM - 08.00 PM Link to join: https://meet.google.com/chq-qqbr-fxd Happy Learning. -NPTEL Team

Week 8 Feedback Form: Introduction to Machine Learning

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/18R3vlAYxYGwda5bbutnUq0R2MSgiMRrbve5cJ7qD5W8/viewform

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: March 13, 2023 - Monday Time:05.00 PM - 07.00 PM Link to join: https://meet.google.com/aqg-nqgx-mgj Happy Learning. -NPTEL Team

Dear learner, Due to unavoidable circumstances, The Problem solving Session organized today (March 11, 2023 - Saturday)(07.00 PM - 09.00 PM) is Postponed to March 13, 2023 - Monday. The G-meet link for the session will be shared before the session. -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/noc23_cs18/unit?unit=95&lesson=96 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/noc23_cs18/unit?unit=95&assessment=164 Assignment-8  for Week-8  is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc23_cs18/unit?unit=95&assessment=155 The assignment has to be submitted on or before Wednesday,[22/03/2023], 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:March 11, 2023 - Saturday Time:03.00 PM - 05.00 PM Link to join: https://meet.google.com/ajc-ehpn-eey Session 2 :  Date: March 11, 2023 - Saturday Time:05.00 PM - 07.00 PM Link to join: https://meet.google.com/epj-ejva-hfv Session 3 :  Date: March 11, 2023 - Saturday Time:07.00 PM - 09.00 PM Link to join: https://meet.google.com/aqg-nqgx-mgj Happy Learning. -NPTEL Team

Introduction to Machine Learning : Assignment solutions for Assignment 6 live now!!

Dear Learner, Assignment 6 solutions are available in the portal, Go through it once . Assignment 6 Solution Link :  https://onlinecourses.nptel.ac.in/noc23_cs18/unit?unit=71&lesson=82 -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: March 10, 2023 - Friday Time:06.00 PM - 08.00 PM Link to join: https://meet.google.com/chq-qqbr-fxd Happy Learning. -NPTEL Team

Week 7 Feedback Form: Introduction to Machine Learning

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: March 05, 2023 - Sunday Time:03.00 PM - 05.00 PM Link to join: https://meet.google.com/aqg-nqgx-mgj 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/noc23_cs18/unit?unit=84&lesson=85 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/noc23_cs18/unit?unit=84&assessment=167 Assignment-7  for Week-7  is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc23_cs18/unit?unit=84&assessment=154 The assignment has to be submitted on or before Wednesday,[15/03/2023], 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 learner, Due to unavoidable circumstances, The Problem solving Session organized tomorrow (March 4, 2023 - Saturday)(07.00 PM - 09.00 PM) is Postponed to March 5, 2023 - Sunday. The G-meet link for the session will be shared before the session. -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:March 04, 2023 - Saturday Time:03.00 PM - 05.00 PM Link to join: https://meet.google.com/ajc-ehpn-eey Session 2 :  Date: March 04, 2023 - Saturday Time:05.00 PM - 07.00 PM Link to join: https://meet.google.com/epj-ejva-hfv 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: March 03, 2023 - Friday Time:06.00 PM - 08.00 PM Link to join: https://meet.google.com/chq-qqbr-fxd Happy Learning. -NPTEL Team

Introduction to Machine Learning : Assignment solutions for Assignment 5 live now!!

Dear Learner, Assignment 5 solutions are available in the portal, Go through it once . Assignment 5 Solution Link :  https://onlinecourses.nptel.ac.in/noc23_cs18/unit?unit=60&lesson=70 -Nptel Team

Week 6 Feedback Form: Introduction to Machine Learning

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:February 25, 2023 - Saturday Time:03.00 PM - 05.00 PM Link to join: https://meet.google.com/ajc-ehpn-eey Session 2 :  Date: February 25, 2023 - Saturday Time:05.00 PM - 07.00 PM Link to join: https://meet.google.com/epj-ejva-hfv Session 3 :  Date: February 25, 2023 - Saturday Time:07.00 PM - 09.00 PM Link to join: https://meet.google.com/aqg-nqgx-mgj 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/noc23_cs18/unit?unit=71&lesson=72 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/noc23_cs18/unit?unit=71&assessment=163 Assignment-6  for Week-6  is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc23_cs18/unit?unit=71&assessment=153 The assignment has to be submitted on or before Wednesday,[08/03/2023], 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: February 24, 2023 - Friday Time:04.30 PM - 06.30 PM Link to join: https://meet.google.com/chq-qqbr-fxd Happy Learning. -NPTEL Team

Introduction to Machine Learning : Assignment solutions for Assignment 4 live now!!

Dear Learner, Assignment 4 solutions are available in the portal, Go through it once . Assignment 4 Solution Link :  https://onlinecourses.nptel.ac.in/noc23_cs18/unit?unit=51&lesson=59 -Nptel Team

Week 5 Feedback Form: Introduction to Machine Learning

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/noc23_cs18/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/noc23_cs18/unit?unit=60&assessment=162 Assignment-5  for Week-5  is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc23_cs18/unit?unit=60&assessment=152 The assignment has to be submitted on or before Wednesday,[01/03/2023], 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:February 18, 2023 - Saturday Time:03.00 PM - 05.00 PM Link to join: https://meet.google.com/ajc-ehpn-eey Session 2 :  Date: February 18, 2023 - Saturday Time:05.00 PM - 07.00 PM Link to join: https://meet.google.com/epj-ejva-hfv Session 3 :  Date: February 18, 2023 - Saturday Time:07.00 PM - 09.00 PM Link to join: https://meet.google.com/aqg-nqgx-mgj Happy Learning. -NPTEL Team

Introduction to Machine Learning : Assignment solutions for Assignment 3 live now!!

Dear Learner, Assignment 3 solutions are available in the portal ,Go through it once . Assignment 3 Solution Link :  https://onlinecourses.nptel.ac.in/noc23_cs18/unit?unit=42&lesson=49 -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: February 17, 2023 - Friday Time:06.00 PM - 08.00 PM Link to join: https://meet.google.com/chq-qqbr-fxd Happy Learning. -NPTEL Team

Week 4 Feedback Form: Introduction to Machine Learning

Introduction to machine learning - problem solving session recording is available.

Dear Learner, We have uploaded the Recorded videos of the Live Interaction Session - 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

Introduction to Machine Learning : Problem solving Session

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:February 10, 2023 - Saturday Time:05.00 PM - 07.00 PM Link to join: https://meet.google.com/epj-ejva-hfv Session 2 :  Date: February 10, 2023 - Saturday Time:07.00 PM - 09.00 PM Link to join: https://meet.google.com/aqg-nqgx-mgj Session 3 :  Date: February 10, 2023 - Saturday Time:03.00 PM - 05.00 PM Link to join: https://meet.google.com/ajc-ehpn-eey 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/noc23_cs18/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/noc23_cs18/unit?unit=51&assessment=166 Assignment-4  for Week-4  is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc23_cs18/unit?unit=51&assessment=151 The assignment has to be submitted on or before Wednesday,[22/02/2023], 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: February 10, 2023 - Friday Time:06.00 PM - 08.00 PM Link to join: https://meet.google.com/chq-qqbr-fxd Happy Learning. -NPTEL Team

Introduction to Machine Learning : Assignment solutions for Assignments 1 & 2 live now!!

Dear Learner, Assignments 1 & 2 solutions are available in the portal ,Go through it once . Assignment 1 solutions :  https://onlinecourses.nptel.ac.in/noc23_cs18/unit?unit=22&lesson=31 Assignment 2 solutions :  https://onlinecourses.nptel.ac.in/noc23_cs18/unit?unit=32&lesson=41 -Nptel Team

Week 3 Feedback Form: Introduction to Machine Learning

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: February 4, 2023 - Saturday Time:05.00 PM - 07.00 PM Link to join: https://meet.google.com/epj-ejva-hfv Session 2 :  Date: February 4, 2023 - Saturday Time:07.00 PM - 09.00 PM Link to join: https://meet.google.com/aqg-nqgx-mgj Session 3 :  Date: February 4, 2023 - Saturday Time:03.00 PM - 05.00 PM Link to join: https://meet.google.com/ajc-ehpn-eey 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/noc23_cs18/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/noc23_cs18/unit?unit=42&assessment=165 Assignment-3  for Week-3  is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc23_cs18/unit?unit=42&assessment=150 The assignment has to be submitted on or before Wednesday,[15/02/2023], 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: February 3, 2023 - Friday Time:06.00 PM - 08.00 PM Link to join: https://meet.google.com/chq-qqbr-fxd Happy Learning. -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. Session 1 :  Start Date: February 3, 2023 When: Every Friday Time: 06.00 PM - 08.00 PM Link to join:  https://meet.google.com/chq-qqbr-fxd Session 2 :  Start Date: February 4, 2023 When: Every Saturday Time: 05.00 PM - 07.00 PM Link to join:   meet.google.com/epj-ejva-hfv Session 3 :  Start Date:  February 4, 2023 When: Every Saturday Time: 07.00 PM - 09.00 PM Link to join:   meet.google.com/aqg-nqgx-mgj Session 4 :  Start Date:  February 4, 2023 When: Every Saturday Time: 03.00 PM - 05.00 PM Link to join:   https://meet.google.com/ajc-ehpn-eey Thank you. -NPTEL team

Week 2 Feedback Form: Introduction to Machine Learning

Introduction to machine learning - week-2 content is live now .

Dear Learners, The lecture videos for Week 2 have been uploaded for the course " Introduction to Machine Learning ".The lectures can be accessed using the following   Link:  https://onlinecourses.nptel.ac.in/noc23_cs18/unit?unit=32&lesson=33 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-2 for Week-2 is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc23_cs18/unit?unit=32&assessment=161 Assignment-2 for Week-2 is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc23_cs18/unit?unit=32&assessment=148 The assignment has to be submitted on or before Wednesday,[08/02/2023], 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

Week 1 Feedback Form: Introduction to Machine Learning

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/12xV5aJuc8r4uCZJVncM-uW6WVEMjFfIw6aSDN58uMbw/viewform Thank you -NPTEL team

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:  https://onlinecourses.nptel.ac.in/noc23_cs18/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/noc23_cs18/unit?unit=22&assessment=160 Assignment-1 for Week-1 is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc23_cs18/unit?unit=22&assessment=147 The assignment has to be submitted on or before Wednesday,[08/02/2023], 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-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/noc23_cs18/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). 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 & Regards   -NPTEL Team

Introduction to Machine Learning - Week 0 content is live now !!

Dear Learners, We welcome you all to this 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/noc23_cs18/unit?unit=16&assessment=146 Please note that this assignment is for practice and it will not be graded. Thanks & Regards   -NPTEL Team

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

Dear Learner, 

Here is the much-awaited announcement on registering for the Jan 2023 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: Apr 30, 2023

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 Mar 17, 2023, 5:00 PM, Exam fees will be Rs. 1000/- per exam .

5. 50% fee waiver for the following categories: 

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. 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: Mar 17, 2023, 5:00 PM :  

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: - 

6. What is your role ? 

11. LAST DATE FOR CANCELLING EXAMS and getting a refund: Mar 17, 2023, 5:00 PM  

12. Click here to view Timeline and Guideline : Guideline

Introduction to Machine Learning: Welcome to NPTEL Online Course - Jan 2023!!

  • 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: April 30, 2023  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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[Week 1-12] NPTEL Introduction To Machine Learning Assignment Answer 2023

NPTEL Introduction To Machine Learning Assignment Answer 2023

NPTEL Introduction To Machine Learning Assignment Answer

Table of Contents

NPTEL Introduction To Machine Learning Week 12 Assignment Answer 2023

Q1. You want to make an RL agent for a game where 2 players compete to win (like Chess and Go). Which among the given would be the best approach for this? Play against best human players Iteratively play against the best (fixed) version of itself Play against a supervised agent trained on demonstrations of best human players Watch thousands of games being played and learn the patterns in an unsupervised manner

Q2. Statement 1: Empirical error is always greater than generalisation error. Statement 2: Training data and test data have different underlying(true) distributions. Choose the correct option: Statement 1 is true. Statement 2 is true. Statement 2 is the correct reason for statement 1. Statement 1 is true. Statement 2 is true. Statement 2 is not the correct reason for statement 1. Statement 1 is true. Statement 2 is false. Both statements are false.

image 63

Q5. Statement A: Reinforcement learning is a type of unsupervised learning. Statement B: Reinforcement learning does not have labels. Both statements are true. Statement B is the correct explanation for statement A. Both statements are true. Statement B is NOT the correct explanation for statement A. Statement A is true. Statement B is false. Statement A is false. Statement B is true. Both statements are false.

Q6. What is a policy in reinforcement learning? A mapping from states to actions A mapping from states to rewards A mapping from actions to rewards A mapping from actions to next state

image 65

NPTEL Introduction To Machine Learning Week 11 Assignment Answer 2023

Q1. What is the update for πk in EM algorithm for GMM?

image 48

Q2. Consider the two statements: Statement 1: The EM algorithm can only be used for parameter estimation of mixture models. Statement 2: The Gaussian Mixture Models used for clustering always outperform k-means and single-link clustering. Which of these are true? Both the statements are true Statement 1 is true, and Statement 2 is false Statement 1 is false, and Statement 2 is true Both the statements are false

Q3. KNN is a special case of GMM with the following properties: (Select all that apply)

Q4. What does soft clustering mean in GMMs? There may be samples that are outside of any cluster boundary. The updates during maximum likelihood are taken in small steps, to guarantee convergence. It restricts the underlying distribution to be gaussian. Samples are assigned probabilities of belonging to a cluster.

Q5. In Gaussian Mixture Models, πi are the mixing coefficients. Select the correct conditions that the mixing coefficients need to satisfy for a valid GMM model.

Q6. What statement(s) are true about the expectation-maximization (EM) algorithm?

  • It requires some assumption about the underlying probability distribution.
  • Comparing to a gradient descent algorithm that optimizes the same objective function as EM, EM may only find a local optima, whereas the gradient descent will always find the global optima
  • The EM algorithm minimizes a lower bound of the marginal likelihood P(D;θ)
  • The algorithm assumes that some of the data generated by the probability distribution are not observed.

Q7. Consider the two statements: Statement 1: The EM algorithm can get stuck at saddle points. Statement 2: EM is guaranteed to converge to a point with zero gradient. Which of these are true? Both the statements are true Statement 1 is true, and Statement 2 is false Statement 1 is false, and Statement 2 is true Both the statements are false

NPTEL Introduction To Machine Learning Week 10 Assignment Answer 2023

1. The pairwise distance between 6 points is given below. Which of the option shows the hierarchy of clusters created by single link clustering algorithm?

Screenshot%20(92)

2. For the pairwise distance matrix given in the previous question, which of the following shows the hierarchy of clusters created by the complete link clustering algorithm.

Screenshot%20(98)

4. Run K-means on the input features of the MNIST dataset using the following initialization:

K Means(nclusters=10,randomstate=seed)

Usually, for clustering tasks, we are not given labels, but since we do have labels for our dataset, we can use accuracy to determine how good our clusters are.

Label the prediction class for all the points in a cluster as the majority true label. E.g. {a,a,b} would be labeled as {a,a,a}

What is the accuracy of the resulting labels? 0.790 0.893 0.702 0.933

image 23

6. a in rand-index can be viewed as true positives(pair of points belonging to the same cluster) and b as true negatives(pair of points belonging to different clusters). How, then, are rand-index and accuracy from the previous two questions related?

  • rand-index = accuracy
  • rand-index = 1.18×accuracy
  • rand-index = accuracy/2
  • None of the above

7. Run BIRCH on the input features of MNIST dataset using Birch(nclusters=10,threshold=1). What is the rand-index obtained?

8. Run PCA on MNIST dataset input features with n components = 2. Now run DBSCAN using DBSCAN(eps=0.5,minsamples=5) on both the original features and the PCA features. What are their respective number of outliers/noisy points detected by DBSCAN?

As an extra, you can plot the PCA features on a 2D plot using matplotlib.pyplot.scatter with parameter c=y−pred (where y−pred is the cluster prediction) to visualise the clusters and outliers.

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NPTEL Introduction To Machine Learning Week 9 Assignment Answer 2023

1. Which of the following best describes the Markov property in a Hidden Markov Model (HMM)?

  • The future state depends on the current state and the entire past sequence of states.
  • The future state depends o n ly on the current state and is independent of the past states, given the current state.
  • The future state depends on the past states and the future states, given the current state.
  • The future state depends only on the past states and is independent of the current state.

2. Statement 1: Probability distributions are valid potential functions. Statement 2: Probability is always strictly positive.

  • Statement 1 is true. Statement 2 is true. Statement 2 is the correct reason for statement 1.
  • Statement 1 is true. Stat e ment 2 is true. Statement 2 is not the correct reason for statement 1.
  • Statement 1 is true. Statement 2 is false.
  • Both statements are false.

w9q3 iml

4. Given graph below:

w9q4 iml

Factorization is:

p(x,y,z)=p(x)p(y|x)p(y|z)

p(x,y,z)=p(y)p(x | y)p(z|y)

p(x,y,z)=p(z)p(z|y)p(x|y)

p(x,y,z)=p(y)p(y|x)p(y|z)

5. For the given graphical model, what is the optimal variable elimination order when trying to calculate P(E=e)?

w9q5

6. Which of the following methods are used for calculating conditional probabilities? (more than one may apply)

  • Viterbi algorithm
  • MAP i nference
  • Variable elimination
  • Belief propagation

7. In the undirected graph given below, which nodes are conditionally independent of each other given a single other node (may be different for different pairs)? Select a l l that apply.

w9q7

NPTEL Introduction To Machine Learning Week 8 Assignment Answer 2023

1. The figure below shows a Bayesian Network with 9 variables, all of which are binary.

a8q1

Which of the following is/are always true for the above Bayesian Network?

  • P(A,B|G)=P(A|G)P(B|G)
  • P(A,I)=P(A)P(I)
  • P(B,H|E,G)=P ( B|E,G)P(H|E,G)
  • P(C|B,F)=P(C|F)

2. Consider the following data for 20 budget phones, 30 mid-range phones, and 20 high-end phones:

a8q2

Consider a phone with 2 SIM card slots and NFC but no 5G compatibility. Calculate the probabilities of this phone being a budget phone, a mid-range phone, and a high-end phone using the Naive Bayes method. The correct ordering of the phone type from the highest to the lowest probability is?

  • Budget, Mid-Range, High End
  • Budget, High E n d, Mid-Range
  • Mid-Range, High End, Budget
  • High End, Mid-Range, Budget

3. A dataset with two classes is plotted below.

a8q3

Does the data satisfy the Naive Bayes assumption?

  • The given da t a is insufficient
  • None of these

4. A company hires you to look at their classification system for whether a given customer would potentially buy their product. When you check the existing classifier on different folds of the training set, you find that it manages a low accuracy of usually around 60%. Sometimes, it’s barely above 50%. With this information in mind, and without using additional classifiers, which of the following ensemble methods would you use to increase the classification accuracy effectively?

  • Committee Machine

5. Which of the following algorithms don’t use learning rate as a hyperparameter?

  • Random Forests

6. Consider the two statements: Statement 1: Bayesian Networks need not always be Directed Acyclic Graphs (DAGs) Statement 2 : Each node in a bayesian network represents a random variable, and each edge represents conditional dependence. Which of these are true?

  • Both the statements are True.
  • Statement 1 is true, and statement 2 is false.
  • Statement 1 is false, a nd statement 2 is true.
  • Both the statements are false.

7. A dataset with two classes is plotted below.

a8q7

  • The gi v en data is insufficient

8. Consider the below dataset:

a8q8

Suppose you have to classify a test example “The ball won the race to the boundary” and are asked to compute P(Cricket |“The ball won the race to the boundary”), what is an issue that you will face if you are using Naive Bayes Classifier, and how will you work around it? Assume you are using word frequencies to estimate all the probabilities.

  • There won’t be a problem, and the probability of P(Cricket |“The ball won the race to the boundary”) will be equal to 1.
  • Problem: A few words that appear at test time do not appear in the dataset.
  • Solution: Sm o othing.
  • Problem: A few words that appear at test time appear more than once in the dataset.
  • Solution: Remove those words from the dataset.

NPTEL Introduction To Machine Learning Week 7 Assignment Answer 2023

1. What is bootstrapping in the context of machine learning?

  • A technique to improve model training speed.
  • A method to reduce the size of the dataset.
  • Creating mu l tiple datasets by randomly sampling with replacement.
  • A preprocessing step to normalize data.

2. Which of the following is NOT a benefit of cross-validation?

  • Reduces the risk of overfitting.
  • Provides a more accurate estimate of model performance.
  • Allows for bett e r understanding of model bias.
  • Increases the size of the training dataset.

3. Bagging is an ensemble method that:

  • Focuses on boosting the performance of a single weak learner.
  • Trains multiple models sequentially, each learning from the mistakes of the previous one.
  • Combin e s predictions of multiple models to improve overall accuracy.
  • Utilizes a committee of diverse models for prediction.

4. Which evaluation measure is more suitable for imbalanced classification problems?

  • Mean Squared Error

5. What does the ROC curve represent?

  • The trade-off between precision and recall.
  • The relationship between accuracy and F1-score.
  • The performance o f a model across various thresholds.
  • The distribution of classes in a dataset.

6. Which ensemble method involves training multiple models in such a way that each model corrects the errors of the previous model?

  • Committee Machines

7. In a ROC curve, what does the diagonal line represent?

  • The perfect classifier
  • Random guessing
  • Trade-off bet w een sensitivity and specificity
  • The ideal threshold for classification

8. In k-fold cross-validation, how is the dataset divided for training and testing?

  • The dataset is randomly shuffled and divided into k equal parts. One part is used for testing and the remaining k-1 parts are used for training.
  • The dataset is split into two equal parts: one for training and the other for testing.
  • The dataset is divided into k eq u al parts. One part is used for testing and the remaining k-1 parts are used for training in each iteration.
  • The dataset is divided into k unequal parts based on data distribution.

9. What is the primary advantage of ensemble methods over individual models?

  • Simplicity of implementation
  • Lower computat i onal complexity
  • Increased Robustness
  • Faster training time

NPTEL Introduction To Machine Learning Week 6 Assignment Answer 2023

1. Which of the following is/are major advantages of decision trees over other supervised learning techniques? (Note that more than one choices may be correct)

  • Theoretical guarantees of performance
  • Higher performance
  • Interpretability of clas s ifier
  • More powerful in its ability to represent complex funct i ons

2. Increasing the pru n ing strength in a decision tree by reducing the maximum depth:

  • Will always result in improved validation accuracy.
  • Will lead to more overfitting.
  • Might lead to underfitting if set t o o aggressively.
  • Will have no impact on the tree’s performance.
  • Will eliminate the need for validation data.

3. Consider th e following statements: Statement 1: Decision Trees are linear non-parametric models. Statement 2 : A decision tree may be used to explain the c o mplex function learned by a neural network.

Both the sta t ements are True. Statement 1 is True, but Statement 2 is False. Statement 1 is Fals e , but Statement 2 is True. Both the statements are False.

4. Consider the follo w ing dataset:

w6q4

What is the initial entropy of Malignant?

5. For the same dataset, what is the info gain of Vaccination?

6. Which of the following machine learning models can solve the XOR problem without any transformations on the input space?

  • Linear Perceptron
  • Neural Networks
  • Decision Tre e s
  • Logistic Regression

7. Statement: Decision Tree is an unsupervised learning algorithm. Reason: The splitting criterion use only the features of the data to calculate their respective measures

  • Statement is True. Reason is True.
  • Statement is T r ue. Reason is False.
  • Statement is False. Reason is True.
  • Statement is False. Reason is False.

8. _____ _ is a measurement of likelihood of an incorrect classification of a new instance for a random variable, if the new instance is randomly classified as per the distribution of class labels from the data set.

  • Gini impurity.
  • Informa ti on gain.
  • None of the above.

9. What is a common indicator of overfitting in a decision tree?

  • The training accuracy is high while the validation accuracy is low.
  • The tree is shallow.
  • The tree has only a few leaf nodes.
  • The tree’s depth matches the nu m ber of attributes in the dataset.
  • The tree’s predictions are consistently biased.

10. Consider a dataset with only one attribute(categorical). Suppose, there are 10 unordered values in this attribute, how many possible combinations are needed to find the best split-point for building the decision tree classifier? (considering only binary splits)

NPTEL Introduction To Machine Learning Week 5 Assignment Answer 2023

1. The perceptron learning algorithm is primarily designed for:

  • Regression tasks
  • Unsupervised learning
  • Clustering ta s ks
  • Linearly separable classification tasks
  • Non-linear classification tasks

2. The last layer of ANN is linear for and softmax for .

  • Regression, Regression
  • Classificat i on, Clas s ification
  • Regression, Classification
  • Classification, Regression

3. Consider the following statement and answer True/False with corresponding reason: The class outputs of a classification problem wit h a ANN cannot be treated independently.

  • True. Due to cross-entropy loss function
  • True. Due to softmax activation
  • False. This is the c a se for regression with single output
  • False. This is the case for regression with multiple outputs

4. Given below is a simple ANN with 2 inputs X1,X2∈{0,1} and edge weights −3,+2,+2

image 49

Which of the following logical functions does it compute?

5. Using the notations used in class, evaluate the value of the neural network with a 3-3-1 architecture (2-dimensional input with 1 node for the bias term in both the layers). The parameters are as follows

image 50

Using sigmoid function as the activation functions at both the layers, the output of the network for an input of (0.8, 0.7) will be (up to 4 decimal places)

6. If the step size in gradient descent is too large, what can happen?

  • Overfitting
  • The model will not converge
  • We can reach maxi m a instead of minima

7. On different initializations of your neural network, you get significantly different values of loss. What could be the reason for this?

  • Some problem in the architecture
  • Incorrect activatio n function
  • Multiple local minima

8. The likelihood L(θ|X) is given by:

  • P(X). P (θ)

9. Why is proper initialization of neural network weights important?

  • To ensure faster convergence during training
  • To prevent overfitting
  • To increase the mod e l’s capacity
  • Initialization doesn’t significantly affect network performance
  • To minimize the number of layers in the network

10. Which of these are limitations of the backpropagation algorithm?

  • It requires error function to be differentiable
  • It requires activation function to be differentiable
  • The ith layer cannot be update d before the update of layer i+1 is complete
  • All of the above
  • (a) and (b) only

NPTEL Introduction To Machine Learning Week 4 Assignment Answer 2023

a4q1

Q2. Which of the following loss functions are convex? (Multiple options may be correct)

  • 0-1 loss (sometimes referred as mis-classification loss)
  • Logistic lo s s
  • Squared error loss

Q3. Which of the following are valid kernel functions?

  • (1+ < x, x’ >) d
  • tanℎ(K 1 <x,x’>+K2)
  • exp(−γ||x−x’| | 2)

a4q5

State true or false: Th e dataset becomes linearly separable after using basis expansion with the following basis function ϕ(x)=[1x 3 ]

Q5. State True or False: SVM cannot classify data that is not linearly separable even if we transform it to a higherdimensional space.

Q6. State True or False: The decision boundary obtained using the perceptron algorithm does not depend on the init i al values of the weights.

Q7. Consider a linear SVM trained with n labeled points in R 2 without slack penalties and resulting in k=2 support vectors, where n>100. By removing one labeled training point and retraining the SVM classifier, what is the max i mum possible number of support vectors in the resulting solution?

Q8. Consider an SVM with a second order polynomial kerne l. Kernel 1 maps each input data point x to K 1 (x)=[x x 2 ]. Kernel 2 maps each input data point x to K 2 (x)=[3x 3×2]. Assume the hyper-parameters are fixed. Which of the following option is true?

  • The margin obtained using K 2 (x) will be larger than the margin obtained using K 1 (x).
  • The margin obtained using K 2 (x) will be smaller than the margin obtained using K 1 (x).
  • The margin obtained using K 2 (x) will be the same as the margin obtained using K 1 (x).

NPTEL Introduction To Machine Learning Week 3 Assignment Answer 2023

1. Which of the following are differences between LDA and Logistic Regression?

  • Logistic Regression is typically suited for binary classification, whereas LDA is directly applicable to multi-class problems
  • Logistic Regression is robust to outliers whereas LDA is sensiti v e to outliers
  • both (a) and (b)

2. We have two classes in our dataset. The two classes have the same mean but different variance.

LDA can classify them perfectly. LDA can NOT classify them perf e ctly. LDA is not applicable in data with these properties Insufficient information

3. We have two classes in our dataset. The two classes have the same variance but different mean.

LDA can classify them perfectly. LDA can NOT classify them perfectly. LDA is not applicable in data with these prop e rties Insufficient information

4. Given the following distribution of data points:

a3q4

What method would you choose to perform Dimensionality Red u ction? Linear Discriminant Analysis Principal Component Analysis Both LDA and/or PCA. None of the above.

5. If log(1−p(x)/1+p(x))=β0+βx Wha t is p(x) ?

p(x)=1+eβ0+βx / eβ0+βx p(x)=1+eβ0+βx / 1−eβ0+βx p(x)=eβ0+βx / 1+eβ0+βx p(x)=1−eβ0+βx / 1+eβ0+βx

a3q6

Red Orange Blue Green

7. Which of these techniques do we use to optimise Logistic Regres s ion:

Least S q uare Error Maximum Likelihood (a) or (b) are equally good (a) and (b) perform very poorly, so we generally avoid using Logistic Regression None of these

8. LDA assumes that the class data is distributed as:

Poisson Unif o rm Gaussian LDA makes no such assumption.

9. Suppose we have two variables, X and Y (the dependent variable), and we wish to find their relation. An expert tells us that relation between the two has the form Y=meX+c. Suppose the samples of the variables X and Y are available to us. Is it possible to apply linear regression to this data to estimate the values of m and c ?

No. Yes. Insufficient information. None of the above.

10. What might happen to our logistic regression model if the number of features is more th a n the number of samples in our dataset?

  • It will remain unaffected
  • It will not find a hyperplane as the decision bound a ry
  • It will over fit

NPTEL Introduction To Machine Learning Week 2 Assignment Answer 2023

1. The parameters obtained in linear reg r ession

  • can take any value in the real space
  • are strictly integers
  • always lie in the range [0,1]
  • can take only non-zero values

2. Suppose that we have N independent variables (X1,X2,…Xn) and the dependent variable is Y . Now imagine that you are applying linear regression by fitting the best fit line using the least square error on thi s data. You found that the correlation coefficient for one of its variables (Say X1) with Y is -0.005.

  • Regressing Yon X1 mostly does not explain away Y .
  • Regressing Y on X1 explains a way Y .
  • The given data is insufficient to determine if regressing Yon X1 explains away Y or not.

3. Which of the following is a limitation of subset selection methods in regression?

  • They tend to produce biased estimates of the regression coefficients.
  • They cannot handle datasets with missing values.
  • They are computationally expensive for large dataset s.
  • They assume a linear relationship between the independent and dependent variables.
  • They are not suitable for datasets with categorical predictors.

4. The relation between studying time (in hours) and grade on the final examination (0-100) in a random sample of students in the Introduction to Machine Learning Class was found to be:Grade = 30.5 + 15.2 (h) How will a student’s g r ade be affected if she studies for four hours?

  • It will go down by 30.4 points.
  • It will go up by 60.8 points .
  • The grade will remain unchanged.
  • It cannot be determined from the information given

5. Which of the statements is/are True?

  • Ridge has sparsity constraint, and it will drive coefficien t s with low values to 0.
  • Lasso has a closed form solution for the optimization problem, but this is not the case for Ridge.
  • Ridge regression does not reduce the number of variables since it never leads a coefficient to zero but only minimizes it.
  • If there are two or more highly collinear variables, Lasso will select one of them randomly

W2Q6

7. Consider the following statements: Statement A: In Forward stepwise selection, in each step, that variable is chosen which has the maximum correlation with the residual, then the residual is regressed on that variable, and it is added to the predictor. Statement B: In Forward stagewise selection, the variables are added one by one to the previously selected variables to produce the best fit till then

  • Statement A is True, and Statement B is False
  • Statement A is False and Statement B is True
  • Both the statements are False.

8. The linear regression model y=a 0 +a 1 x 1 +a 2 x 2 +…….+a p x p is to be fitted to a set of N training data points having p attributes each. Let X be N×(p+1) vectors of input values (augmented by 1‘s), Y be N×1 vector of target values, and θθ be (p+1)×1 vector of parameter values (a 0 ,a 1 ,a 2 ,…,a p . If the sum squared error is minimized for obtainin g the optimal regression model, which of the following equation holds?

  • X T Xθ=X T Y

9. Which of the following statements is true regarding Partial Least Squares (PLS) reg r ession?

  • PLS is a dimensionality reduction technique that maximizes the covariance betw e en the predictors and the dependent variable.
  • PLS is only applicable when there is no multicollinearity among the independent variables.
  • PLS can handle situations where the number of predictors is larger than the number of observations.
  • PLS estimates the regression coefficients by minimizing the residual sum of squares.
  • PLS is based on the assumption of normally distri b uted residuals.
  • All of the above.

10. Which of the following statements about principal components in Principal Component Regressi o n (PCR) is true?

  • Principal components are calculated based on the correlation matrix of the original predictors.
  • The first principal component explains the largest proportion of the variation in the dependent variable.
  • Principal components are linear combinations of the original predictors that are uncorrelated with each other.
  • PCR selects the principal components with the highest p-values for i nclusion in the regression model.
  • PCR always results in a lower model complexity compared to ordinary least squares regression.

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)

w1q8

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

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NPTEL Introduction to Machine Learning Assignment Answers Week 7 2022 IITKGP

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Are you looking for help in Machine Learning NPTEL week 7 assignment answers? So, here in this article, we have provided Machine Learning week 7 assignment answer’s hint.

NPTEL Introduction to Machine Learning Assignment Answers Week 7

Q1. Which of the following option is/are correct regarding the benefits of ensemble model?

1. Better performance

2. More generalized model

3. Better interpretability

a. 1 and 3 b. 2 and 3 c. 1 and 2 d. 1, 2 and 3

Answer: c. 1 and 2

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Q2. In AdaBoost, we give more weights to points having been misclassified in previous iterations. Now, if we introduced a limit or cap on the weight that any point can take (for example, say we introduce a restriction that prevents any point’s weight from exceeding a value of 10). Which among the following would be an effect of such a modification?

A. We may observe the performance of the classifier reduce as the number of stagesincrease.

B. It makes the final classifier robust to outliers.

C. It may result in lower overall performance.

D. None of these.

Answer : Option B and C.

Q3. Which among the following are some of the differences between bagging and boosting?

a.In bagging we use the same classification algorithm for training on each sample of the data, whereas in boosting, we use different classification algorithms on the different training data samples.

b. Bagging is easy to parallelize whereas boosting is inherently a sequential process.

c. In bagging we typically use sampling with replacement whereas in boosting, we typically use weighted sampling techniques.

d. In comparison with the performance of a base classifier on a particular dataset, bagging will generally not increase the error whereas as boosting may leadto an increase in the error.

Answer : Option B, C and D.

Q4. What is the VC-dimension of the class of sphere in a 3-dimensional plane?

Answer: a. 3

Q5. Considering the AdaBoost algorithm, which among the following statements is true?

a. In each stage, we try to train a classifier which makes accurate predictions on anysubset of the data points where the subset size is at least half the size of the data set.

b. In each stage, we try to train a classifier which makes accurate predictions on a subset of the data points where the subset contains more of the data points whichwere misclassified in earlier stages.

c. The weight assigned to an individual classifier depends upon the number of data points correctly classified by the classifier.

d. The weight assigned to an individual classifier depends upon the weighted sumerror of misclassified points for that classifier.

Answer: Option B and D.

Q6. Suppose the VC dimension of a hypothesis space is 6. Which of the following are true?

a. At least one set of 6 points can be shattered by the hypothesis space.

b. Two sets of 6 points can be shattered by the hypothesis space.

c. All sets of 6 points can be shattered by the hypothesis space.

d. No set of 7 points can be shattered by the hypothesis space.

Answer: Option A and D.

Q7. Ensembles will yield bad results when there is a significant diversity among the models. Write True or False.

Answer: b. False

Q8. Which of the following algorithms are not an ensemble learning algorithm?

a. Random Forest

b. Adaboost

c. Gradient Boosting

d. Decision Tress

Answer: d. Decision Tress

Q9. Which of the following can be true for selecting base learners for an ensemble?

a. Different learners can come from same algorithm with different hyper parameters

b. Different learners can come from different algorithms.

c. Different learners can come from different training spaces

d. All of the above.

Answer : d. All of the above.

Q10. Generally, an ensemble method works better, if the individual base models have _____________?

Note: Individual models have accuracy greater than 50%

a. Less correlation among predictions

b. High correlation among predictions

c. Correlation does not have an impact on the ensemble output

d. None of the above.

Answer: a. Less correlation among predictions

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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

NPTEL Introduction to Machine Learning Assignment 1 Answers 2023

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.

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NPTEL Introduction to Machine Learning Assignment 1 Answers 2023

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:

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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

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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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NPTEL INTRODUCTION TO MACHINE LEARNING ASSIGNMENT 1 Answers 2022

  • July 16, 2022 July 16, 2022

NPTEL INTRODUCTION TO MACHINE LEARNING ASSIGNMENT 1

NPTEL INTRODUCTION TO MACHINE LEARNING ASSIGNMENT 1 Answers :- Hello students in this article we are going to share NPTEL The Joy of Computing using Python assignment week 1 answers. All the Answers provided below to help the students as a reference, You must submit your assignment at your own knowledge.

About INTRODUCTION TO MACHINE LEARNING Course:-

With the growth of Information and Communication Technology, there is a need to develop large and complex software. Further, those software should be platform independent, Internet enabled, easy to modify, secure, and robust. To meet this requirement object-oriented paradigm has been developed and based on this paradigm the Java programming language emerges as the best programming environment.

Criteria to get Certificate:-

This course is a week 12 course the best of 8 out 12 assignments marks will be calculated for final result.

Below are mentioned criteria for final result

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.

Below you can find NPTEL INTRODUCTION TO MACHINE LEARNING Assignment 1 Answers

NPTEL INTRODUCTION TO MACHINE LEARNING Assignment 1 Answers 2022:-

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.

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)

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

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

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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Disclaimer: We do not claim 100% surety of answers, these answers are based on our sole knowledge, and by posting these answers we are just trying to help students, so we urge do your assignment on your own.

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