nptel introduction to machine learning assignment answers week 12

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nptel introduction to machine learning assignment answers week 12

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[Week 1-12] NPTEL Introduction To Machine Learning Assignment Answer 2023

Course Content

Week 1 answers 2023, week 1 assignment answer, week 2 answers 2023, week 2 assignment answer, week 3 answers 2023, week 3 assignment answer, week 4 answers 2023, week 4 assignment answer, week 5 answers 2023, week 5 assignment answer, week 6 answers 2023, week 6 assignment answer, week 7 answers 2023, week 7 assignment answer, week 8 answers 2023, week 8 assignment answer, week 9 answers 2023, week 9 assignment answer, week 10 answers 2023, week 10 assignment answer, week 11 answers 2023, week 11 assignment answer, week 12 answers 2023, week 12 assignment answer, student ratings & reviews.

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Introduction to Machine Learning

₹ 3,000.00

Prof. Balaraman Ravindran IIT Madras

*Additional GST and optional Exam fee are applicable.

Description

Additional information, certification process, course details.

Reviews (2)

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

INTENDED AUDIENCE

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

PREREQUISITES

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

INDUSTRY SUPPORT

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

ABOUT THE INSTRUCTOR

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

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

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

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

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

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

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

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

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

CERTIFICATE TEMPLATE

Week 1: Introduction: Statistical Decision Theory – Regression, Classification, Bias Variance Week 2: Linear Regression, Multivariate Regression, Subset Selection, Shrinkage Methods, Principal Component Regression, Partial Least squares Week 3: Linear Classification, Logistic Regression, Linear Discriminant Analysis Week 4: Perceptron, Support Vector Machines Week 5: Neural Networks – Introduction, Early Models, Perceptron Learning, Backpropagation, Initialization, Training & Validation, Parameter Estimation – MLE, MAP, Bayesian Estimation Week 6: Decision Trees, Regression Trees, Stopping Criterion & Pruning loss functions, Categorical Attributes, Multiway Splits, Missing Values, Decision Trees – Instability Evaluation Measures Week 7: Bootstrapping & Cross Validation, Class Evaluation Measures, ROC curve, MDL, Ensemble Methods – Bagging, Committee Machines and Stacking, Boosting Week 8: Gradient Boosting, Random Forests, Multi-class Classification, Naive Bayes, Bayesian Networks Week 9: Undirected Graphical Models, HMM, Variable Elimination, Belief Propagation Week 10: Partitional Clustering, Hierarchical Clustering, Birch Algorithm, CURE Algorithm, Density-based Clustering Week 11: Gaussian Mixture Models, Expectation Maximization Week 12: Learning Theory, Introduction to Reinforcement Learning, Optional videos (RL framework, TD learning, Solution Methods, Applications)

BOOKS AND REFERENCES:

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

2 reviews for Introduction to Machine Learning

biswajit – March 11, 2022

this is a very good course.

CHETHAN MS – October 14, 2022

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Feedback for Introduction to Machine Learning

Dear student We are glad that you have attended the NPTEL online certification course. We hope you found the NPTEL Online course useful and have started using NPTEL extensively. In this regard, we would like to have a 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://goo.gl/forms/LI80jZ9awdRaqrBJ3 We thank you for your valuable time and feedback. Thanks & Regards, NPTEL Team

All the best!!!

Dear Participant, All the best for your final exam. You will do very well. Don't skip your tomorrow's breakfast! Best, Course team.

Introduction to Machine Learning :Reminder for Assignment

Dear Participants, We have received "178" submission for "Assignment 12"so far. The due date for the Assignment 12 is fast approaching. If you are yet to attempt it, do it at the earliest so as not to miss the submission deadline of "2018-04-18, 23:59 IST" also please note that the marks of the assignments will be considered in evaluation of the final grade. -NPTEL Team

Introduction to Machine Learning : Hall ticket for April 28/29 NPTEL Online Certification exams is now available for download

Dear Candidate: If you have registered and paid successfully for the April28/29 NPTEL Online certification exams, your Admit Card/Hall ticket is now available at http://nptelonlinecourses.i itm.ac.in/ Login with your registered Google-enabled account email id (used to access the course on the portal and used for registering for the exam) and its password to access the link. Click on April exam to download the hall ticket(s) for the April28/29 exams, from the link. 2. You can also download it from the alternate link given below: https://cdn3.digialm.com//EFor ms/configuredHtml/885/54105/lo gin.html Please try to access both the links to download your hall ticket: if you are not able to find your hall ticket in both the links, kindly write to us at [email protected] Note that you will be able to login here, only if you have a valid registration for exams on April 28/29, 2018. Please download hall ticket, take a print out and along with a original ID card, carry it with you to the exam venue (The exam centre address is displayed in the hall ticket). Read all instructions given in the admit card/ hall ticket carefully and follow them. Download hall tickets for all the exams you have registered for - in case you don't get the hall tickets for any particular course, please write to [email protected] . IMPORTANT: 1. Change of shift, course, exam center, exam city is NOT POSSIBLE. 2. You WILL NOT BE ALLOWED to write the exam if you are showing up at the exam center which is not allocated to you by mistake or by intention. 3. Candidates will not be allowed to write the exam without Hall ticket and proper id proof. Proof on the mobile phones or soft copy of hall ticket is NOT PERMITTED. NPTEL Team wishes you the very best for the certification exam.

Dear Participants, The Due Date For Assignment 11 is already Closed. Assignment 11: Submissions "501" We have received "83" submissions for "Assignment 12" so far. The due date for the Assignment 12 is fast approaching. If you are yet to attempt it, do it at the earliest so as not to miss the submission deadline of "2018-04-18, 23:59 IST" also please note that the marks of the assignments will be considered in evaluation of the final grade. -NPTEL Team

Introduction to Machine Learning : Week 12 Feedback

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. Would help us tremendously in gauging learner experience. Here is the link to the form: http://nptel.ac.in/noc/nocprofile/super_admin/weekly_feedback/form_login.php Thank you. -NPTEL team

Dear Participants, We have received "157" submissions for "Assignment 11" so far. The due date for the Assignment 11 is fast approaching. If you are yet to attempt it, do it at the earliest so as not to miss the submission deadline of "2018-04-11, 23:59 IST" also please note that the marks of the assignments will be considered in evaluation of the final grade. -NPTEL Team

NOC18: Introduction to Machine Learning- Week 12 content released

Dear Participant, The content for week 12 has been released. Please go through the videos. The content also includes an assignment containing 7 MCQs. The assignment is due on April 18, 11:55 PM IST. You can make multiple submissions of the assignment and your final submission before the deadline will be considered for grading. This is your last assignment for this course. Do Well! All the Best!

Dear Participants, The Due Date For Assignment 10 is already Closed. Assignment 10: Submissions "504" We have received "100" submissions for "Assignment 11" So far The due date for the Assignment 11 is fast approaching. If you are yet to attempt it, do it at the earliest so as not to miss the submission deadline of "2018-04-11, 23:59 IST" also please note that the marks of the assignments will be considered in evaluation of the final grade. -NPTEL Team

Introduction to Machine Learning : Week 11 Feedback

Dear Participants, The Due Date For Assignment 9 is already Closed. Assignment 9: Submissions "554" We have received "145" submissions for "Assignment 10" So far The due date for the Assignment 10 is fast approaching. If you are yet to attempt it, do it at the earliest so as not to miss the submission deadline of "2018-04-04, 23:59 IST" also please note that the marks of the assignments will be considered in evaluation of the final grade. -NPTEL Team

NOC18: Introduction to Machine Learning- Week 11 content released

Dear Participant, The content for week 11 has been released. Please go through the videos. The content also includes an assignment containing 6 MCQs. The assignment is due on April 11, 11:55 PM IST. You can make multiple submissions of the assignment and your final submission before the deadline will be considered for grading. All the Best!

NOC18: Introduction to Machine Learning- Assignment 6,7,8,9 solution posted

Hi participant, Solutions for assignment 6,7,8 and 9 have been posted on the portal. You can access it from the content of respective weeks. If you are having any doubt regarding the solution feel free to post it on the discussion forum. Regards, Tarun Kumar

Introduction to Machine Learning :Week 10 Feedback

Dear Participants, We have received "179" submissions for "Assignment 9" so far. The due date for the Assignment 9 is fast approaching. If you are yet to attempt it, do it at the earliest so as not to miss the submission deadline of "2018-03-28, 23:59 IST." also please note that the marks of the assignments will be considered in evaluation of the final grade. -NPTEL Team

NOC18: Introduction to Machine Learning- Week 10 content released

Dear Participant, The content for week 10 has been released. Please go through the videos. The content also includes an assignment containing 10 MCQs. The assignment also includes programming questions so you are advised to start as early as possible. The assignment is due on April 4, 11:55 PM IST. You can make multiple submissions of the assignment and your final submission before the deadline will be considered for grading. All the Best!

Dear Participants, The Due Date For Assignment 8 is already Closed. Assignment 8: Submissions "571" We have received "89" submissions for "Assignment 9" so far. The due date for the Assignment 9 is fast approaching. If you are yet to attempt it, do it at the earliest so as not to miss the submission deadline of "2018-03-28, 23:59 IST." also please note that the marks of the assignments will be considered in evaluation of the final grade. -NPTEL Team

Introduction to Machine Learning : Week 9 Feedback

NOC18: Introduction to Machine Learning- Week 9 content released

Dear Participant, The content for week 9 has been released. Please go through the videos. The content also includes an assignment containing 7 MCQs. The assignment is due on March 28, 11:55 PM IST. You can make multiple submissions of the assignment and your final submission before the deadline will be considered for grading. Note: Since this assignment has only 7 questions, try to complete it as soon as possible. The next assignment will include questions based on programming. All the Best!

Dear Participants, We have received "174" submissions for "Assignment 8"so far. The due date for the Assignment 8 is fast approaching,If you are yet to attempt it, do it at the earliest so as not to miss the submission deadline of "2018-03-21, 23:59 IST." also please note that the marks of the assignments will be considered in evaluation of the final grade. -NPTEL Team

Dear Participants, The Due Date For Assignment 7 is already Closed. Assignment 7: completed 708 we have recieved Assignment 8: Submissions "76"so far The due date for the assignment 8 is fast approaching. If you are yet to attempt it, do it at the earliest so as not to miss the submission deadline of "2018-03-21, 23:59" also please note that the marks of the assignments will be considered in evaluation of the final grade. -NPTEL Team

NOC 18: Extending registration till March 15th - 1:00 PM

Dear Participant, There seems to be some technical issue with the exam registration form. We are trying to find out the reason and resolve the same. Hence we are extending registration till March 15, 2018 - 1 pm. Thanks!

Last date for Exam registration extended till March 15, 2018 - 1 pm (Thursday) - Hurry up and register today!!

Dear Candidate, Due to a technical issue, there was some problem with the exam form since yesterday. Now the issue has been fixed. The last date for exam registration has been extended till March 15, 2018 - 1 pm (Thursday) Last date for making the payment individually - March 16, 2018 - 1:00 PM ( Friday ) If you have not yet registered for the exam, login to the form and apply for the exam today! If you have filled the form but not done the payment, Please complete the payment before March 16 1:00 pm . Register for the exam today at: http:// nptelonlinecourses.iitm.ac.in/ For other details about exam registration, please check our previous announcement. NOTE : NO EXTENSIONS BEYOND THESE DATES WILL BE GIVEN. -NPTEL Team

Introduction to Machine Learning : Week 8 Feedback

Dear Participants, We have received "205" submissions for "Assignment 7" so far. The due date for the Assignment 7 is fast approaching. If you are yet to attempt it, do it at the earliest so as not to miss the submission deadline of " 2018-03-14, 23:59 IST." also please note that the marks of the assignments will be considered in evaluation of the final grade. -NPTEL Team

NOC18: Introduction to Machine Learning- Week 8 content released

Dear Participant, The content for week 8 has been released. Please go through the videos. The content also includes an assignment containing 10 MCQs. The assignment is due on March 21, 11:55 PM IST. You can make multiple submissions of the assignment and your final submission before the deadline will be considered for grading. All the Best!

Dear Participants, The Due Date For Assignment 6 is Already Closed Assignment 6 Submissions:"881" We have received "107" submission for "Assignment 7"so far. The due date for the Assignment 7 is fast approaching. If you are yet to attempt it, do it at the earliest so as not to miss the submission deadline of " 2018-03-14, 23:59 IST" also please note that the marks of the assignments will be considered in evaluation of the final grade. -NPTEL Team

NOC18: Introduction to Machine Learning- Assignment 5 solution posted

Hi participant, Assignment 5 solution has been posted on the portal. You can access it from the week-5 content. You can also access it through this link . If you are having any doubt regarding the solution feel free to post it on the discussion forum. Regards, Tarun Kumar

Exam and Certificate Format : Introduction to Machine Learning

Exam and Certificate Format Dear student The certification exam will be conducted at designated centres in the city chosen by you. You have to register for the exam by filling up the form, paying the exam fee, appear in person and score >= 40% to get the certificate. Register for the exam today at: http://nptelonlinecourses.iitm.ac.in/ Exam registration form closes on March 14, 2018 - 5 pm (Wednesday). For other details about exam registration, please check our previous announcement. Type of exam: Computer based exam You will have to appear at the allotted exam centre 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 allotted exam center details in the hall ticket. 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 The hall ticket will be available for download tentatively around 10 - 16th April 2018. We will notify the same through email. FINAL CERTIFICATE: The final score = 25% assignment score + 75% final certification exam score. The final score will determine if you will/will not receive a certificate. 1. Final score < 40%: NO certificate 2. Final score between 40% -59%: Certificate of type "Successfully completing the course" 3. Final score between 60% -89%: Certificate with tag "Elite" printed at the top 4. Final score of 90% and above: Certificate with "Elite" tag and the gold medal printed on it. Please click the link here for certificate format: https://goo.gl/ZogyXm -NPTEL Admin

Introduction to Machine Learning :Week 7 Feedback

Dear Participants, We have received "260" submissions for "Assignment 6"so far. The due date for the Assignment 6 is fast approaching. If you are yet to attempt it, do it at the earliest so as not to miss the submission deadline of " 2018-03-07, 23:59 IST." also please note that the marks of the assignments will be considered in evaluation of the final grade. -NPTEL Team

Last date for Exam registration extended - Hurry up and register today!!

Dear Candidates Last date for exam registration has been extended till March 14, 2018 - 5 pm ( Wednesday ) Last date for making the payment individually - March 15, 2018 - 5 PM ( Thursday ) The certification exam will be conducted at designated centers in the city chosen by you. You have to register for the exam by filling up the form, paying the exam fee and appear in person to get the certificate. Hard and soft copy of Certificate will be awarded only to those candidates who register for the exam, attend the certificate examination and whose Final score > 40% Register for the exam today at: http://nptelonlinecourses. iitm.ac.in/ For other details about exam registration, please check our previous announcement. -NPTEL Team

NOC18: Introduction to Machine Learning- Week 7 content released

Dear Participant, The content for week 7 has been released. Please go through the videos. The content also includes an assignment containing 10 MCQs. The assignment is due on March 14, 11:55 PM IST. You can make multiple submissions of the assignment and your final submission before the deadline will be considered for grading. All the Best! Regards, Course TAs

Dear Participants, we have Recieved Submission For assignment 5 : 1005 Submission For assignment 6 : 110 so far. The due date for the assignment 6 is fast approaching. If you are yet to attempt it, do it at the earliest so as not to miss the submission deadline of "2018-03-07, 23:59" also please note that the marks of the assignments will be considered in evaluation of the final grade. -NPTEL Team

Introduction to Machine Learning :Week 6 Feedback

Dear Participants, Due date for assignment 4 is already closed. Submission for Assignment 4: 1249 We have received "250" submissions for "Assignment 5" so far. The due date for the Assignment 5 is fast approaching. If you are yet to attempt it, do it at the earliest so as not to miss the submission deadline of "2018-02-28, 23:59 IST." also please note that the marks of the assignments will be considered in evaluation of the final grade.

NOC18: Introduction to Machine Learning- Assignment 2,3,4 solution posted

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NOC18: Introduction to Machine Learning- Week 6 content released

Dear Participant, The content for week 6 has been released. Please go through the videos. The content also includes an assignment containing 10 MCQs. The assignment is due on March 7, 11:55 PM IST. You can make multiple submissions of the assignment and your final submission before the deadline will be considered for grading. All the Best! Regards, Course TAs

NOC 18: Introduction to Machine Learning -- Lecture on Optimization

Hi, In one of the lecture, Prof. mentions about the Convex optimization tutorial. It can be found on this link . Feel free to post your doubts on the forum. Regards, Course TAs

Dear Participants, Due date for assignment 1, 2 and 3 are already closed. Submission for Assignment 1: 3306 Submission for Assignment 2: 2265 Submission for Assignment 3: 1482 Submission for Assignment 4: 599 Due date for assignment 4 is "2018-02-23,23:59"If you are yet to attempt it, do it at the earliest so as not to miss the submission deadline We have received "85" submissions for "Assignment 5" so far. The due date for the assignment 5 is fast approaching. If you are yet to attempt it, do it at the earliest so as not to miss the submission deadline of "2018-02-28,23:59" also please note that the marks of the assignments will be considered in evaluation of the final grade. -NPTEL Team

Assignment 4 deadline has been extended

Hi, Assignment 4 deadline has been extended to Feb 23, 11:55 pm. In future, we will follow the actual schedule (release assignment on Monday with next Wednesday as the deadline). Best, Tarun Kumar

Introduction to Machine Learning:Week 5 Feedback

Introduction to machine learning - reminder for assignment.

Dear Participants, Due date for assignment 1, 2 and 3 are already closed. Submission for Assignment 1: 3306 Submission for Assignment 2: 2265 Submission for Assignment 3: 1482 We have received "265" submissions for "Assignment 4" so far. The due date for the assignment 4 is fast approaching. If you are yet to attempt it, do it at the earliest so as not to miss the submission deadline of " 2018-02-21, 23:59 IST." also please note that the marks of the assignments will be considered in evaluation of the final grade. -NPTEL Team

NOC18: Introduction to Machine Learning- Week 5 content released

Dear Participant, The content for week 5 has been released. Please go through the videos. The content also includes an assignment containing 8 MCQs. The assignment is due on Feb 28, 11:55 PM IST. You can make multiple submissions of the assignment and your final submission before the deadline will be considered for grading. All the Best! Regards, Course TAs

Week 4 Feedback

Noc18: introduction to machine learning- week 4 content released.

Dear learner, The content for week 4 has been released. Please go through the videos. The content also includes an assignment containing 6 MCQs. There are two theoretical questions (of 1 mark each) and 4 programming questions (of 2 marks each). The assignment is due on Feb 21, 11:55 PM IST. You can make multiple submissions of the assignment and your final submission before the deadline will be considered for grading. We recommend you to use Python as the programming language though we don't restrict you from using any other programming language. For the beginners, following videos may help: 1) iPython for Ubuntu 2) IPython for Windows 3) Get started with sklearn Feel free to ask any questions on the forum. All the Best! Regards, Tarun Kumar

NOC18: Introduction to Machine Learning- Assignment 1 solution posted

Hi participant, Assignment 1 solution has been posted on the portal. You can access it from the week-1 content. You can also access it through this link . Regards, Tarun Kumar

Assignment 2 deadline has been extended

Hi, Assignment 2 deadline has been extended to Feb 9, 11:55 pm. This extension is because of the late submission date of assignment 1. In future, we will follow the actual schedule (release assignment on Monday with next Wednesday as the deadline). Best, Tarun Kumar

Week 3 Feedback

NOC18: Introduction to Machine Learning- Some common questions

Hi, Here are answers to some common questions being asked on the forum: 1) Assignment 0 It will not be graded. We will grade Assignment 1 to Assignment 12. 2) Assignment 1 Deadline for assignment 1 is Feb 5, 11:50 pm 3) Download course material: (videos, unofficial transcript) http://nptel.ac.in/courses/106106139/1 4) Derivation of Bias-Variance split: https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff 5) Programming assignments for the course: We will release our first programming assignment with the content of week-4. We don't restrict participants from using any programming language. But we recommend you to use Python - since it has large support from ML related libraries. 6) How and why do we normalize? https://stats.stackexchange.com/questions/41704/how-and-why-do-normalization-and-feature-scaling-work 7) Similarity in clustering There can be different notions of similarity. Later in the course, we will cover this in detail. 8) Joint probability distribution https://en.wikipedia.org/wiki/Joint_probability_distribution Feel free to ask any question :) Regards, NPTEL TAs

NOC18: Introduction to Machine Learning- Week 3 content released

Dear Participants, The content for week 3 has been released. Please go through the videos. The content also includes an assignment containing 10 MCQs. The assignment is due on Feb 14, 11:59 PM IST. You can make multiple submissions of the assignment and your final submission before the deadline will be considered for grading. All the Best! Regards, Course TAs

Week 2 Feedback

Week 1 feedback.

Dear learner Thank you for enrolling to this NPTEL course and we hope you have gone through the contents for this week and also attempted the assignment. We value your feedback and wish to know how you found the videos and the questions asked - whether they were easy, difficult, as per your expectations, etc We shall use this to make the course better and we can also know from the feedback which concepts need more explanation, etc. Please do spare some time to give your feedback - comprises just 5 questions - should not take more than a minute, but makes a lot of difference for us as we know what the learners feel. Here is the link to the form: http://nptel.ac.in/noc/nocprofile/super_admin/weekly_feedback/form_login.php Thank you. - NPTEL team

NOC18: Introduction to Machine Learning- Week 2 content released

Dear Participants, The content for week 2 has been released. Please go through the videos. The content also includes an assignment containing 10 MCQs. The assignment is due on Feb 7, 11:59 PM IST . Please note that usually assignments are released on Monday and are due the next Wednesday (10 days). You can make multiple submissions of the assignment and your final submission before the deadline will be considered for grading. All the Best! Regards, Course TAs

NOC18: Introduction to Machine Learning- Week 1 content released

Dear Participant, The content for week 1 has been released. Please go through the videos. The content also includes an assignment containing 10 MCQs. The first assignment is due on Feb 5, 11:55 PM IST . Please note that usually assignments are released on Monday and are due the next Wednesday (10 days). You can make multiple submissions of the assignment and your final submission before the deadline will be considered for grading. All the Best! Regards, Course TAs

Reminder 1:REGISTER TODAY - CERTIFICATION EXAM FORM IS NOW OPEN!

Dear Student: Here is the much-awaited announcement on registering for the April 2018 certification exam . The registration for the certification exam is open only to students who have enrolled in the course. Registration is open from 09th January 2018 (Tuesday) until March 07, 2018 10:00 AM (Wednesday) . The certification exam registration URL is: http://nptelonlinecourses.iitm.ac.in/ (If you want to register for the exam for this course, login here using the same email id used to enroll to the course) Dates of exam: April 28 and April 29, 2018 Session: [ Forenoon ] For this course there is no ” Afternoon ” Session. You can register for a maximum of 4 course exams (same day of exam – 2 sessions. Same center will be allocated for both the sessions). Exam Session time: Forenoon: 9.00 AM -12.00 PM ; Afternoon: 2.00 PM - 5.00 PM Examination Cities: The exam is to be conducted in several cities across India whose list is available on the registration form. Click here to access the list of exam cities: http://nptel.ac.in/pdf/examcities_final.pdf Registration fees: Rs 1100/ - (Students belonging to the SC/ST category can avail a 50% fee waiver - please select Yes for the SC/ST option and upload the correct Community certificate) Students belonging to the PwD category can avail a 50% fee waiver - please select Yes for the option and upload the relevant Disability certificate. Mode of payment: Online payment - debit card/credit card/net banking or via SPOC of college HALL TICKET: The hall ticket will be available for download tentatively between 10 - 16th April 2018 . We will confirm the same through an announcement once it is published. Final score on certificate: 25% of assignment score + 75% of certification exam score. Award of certificate: Hard and soft copy of Certificate will be awarded only to those candidates who register for the exam, attend the certificate examination and whose Final score >= 40% The final score, assignment score and exam score will be printed on the certificate. The certificate will also have a link to the NPTEL website ( http://nptel.ac.in/noc ), where on logging in, your scores and e-certificate will be available for verification (Appropriate announcements will be made). Please do regularly submit assignments to get a good final score. IMPORTANT NOTES 1) 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. No changes will be entertained after that. 2) FOR CANDIDATES WHO ARE PAYING VIA THE LOCAL CHAPTER OF YOUR COLLEG E:- 1. In the exam form, you will fill all the details and also upload photo, signature. 2. Ensure that you had selected your college name correctly from the drop-down list in the form. 3. Payment of exam fees - click on the tab - 'PAY VIA SPOC'. The SPOC has to now confirm to NPTEL that he/she will pay fees on your behalf. If the SPOC says NO, you will be intimated via email. Then it becomes your responsibility to come back to the exam form and make the payment. If you do not pay the exam fees within the prescribed time, you will not be able to write the exam. Please read the instructions carefully before submitting the form. In case of any queries please write to us at [email protected] Thanks & Regards, NPTEL TEAM

Assignment 0 - Self evaluation

<7, we strongly recommend that you take up courses in Linear Algebra and Probability (for example : Linear Algebra , Probability )
8 and above, make sure that you fill in some gaps which might have remained in your understanding of the material. Our tutorials might be helpful in this regard.

Welcome to NPTEL Course: Introduction to Machine Learning

Dear student Welcome to NPTEL Online Courses and Certification! Thank you for signing up for our online course " Introduction to Machine Learning ". We wish you an enjoyable and informative learning experience. The course will begin on January 22,2018. When content is released on the portal, you will get an email alerting you. 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. We will open registration for the exam soon after the course starts. A form has to be filled and the certification exam has a fee, which needs to be paid at the time of registration. Everyone who has signed-up for the course, including those who do not register for the exam, will continue to have access to the course contents. There are two sections on the portal apart from the course content and assignment sections: • The announcement list which will only have messages from course instructors and teaching assistants - regarding the lessons, assignments, exam registration, hall tickets etc. • The discussion forum which is for everyone to ask questions and interact - If you have any questions regarding the technical content in the lectures, any doubts in the assignments or any question related to the exam, registration, hall tickets, results, etc, kindly write about this in the forum and the course instructor/TA will respond to it. Please use this well and participate to benefit from the course. Details regarding the course: Name of the course: Introduction to Machine Learning Course url: https://onlinecourses.nptel.ac.in/noc18_cs26/ Course duration :12 Weeks Date and Time of Exams: April 28 (Saturday) and April 29 (Sunday) : Morning session 9am to 12 noon; Exam for this course will be available in one session on both 28 and 29 April. Final List of exam cities will be available in exam registration form. Exam registration url - Will be announced shortly Once again, thank you for your interest in our online courses and certification. Happy learning - NPTEL team.". We wish you an enjoyable and informative learning experience.

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

Dear Candidate, Here is a golden opportunity for those who had previously enrolled in this course during the Jan 2022 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 2022 and we are giving you another chance to write the exam in October 2022 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: October 29 2022 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 in the previous semester 6. If you do enroll to July 2022 course, we will take the best average assignment scores/non-proctored programming exam score across the two semesters NOTE: 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 that were conducted in Jan 2022 to become eligible for the e-certificate, wherever applicable. If not, please submit assignments again in the July 2022 course & and also participate in the non-proctored programming exams to become eligible for the e-certificate. We will not be having new assignments or unproctored exams in the previous semester's(Jan 2022) course. You can also submit assignments again and participate in the non-proctored programming exams if you want to take fresh assignments or need to improve your previous scores. RECOMMENDATION: If you want to take new assignments and an unproctored exam or brush up on your lessons for the exam, please enroll in the July 2022 course. LINK to enroll in the current course: https://onlinecourses.nptel.ac.in/noc22_cs73/preview 7. Exam fees: If you register for the exam and pay before Sep 12, 2022, 10:00 AM , Exam fees will be Rs. 1000/- per exam. If you register for exam before Sep 12, 2022, 10:00 AM and have not paid or if you register between Sep 12, 2022, 10:00 AM & Sep 16, 2022, 10:00 AM , Exam fees will be Rs. 1500/- per exam 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: Sep 16, 2022, 10:00 AM (Friday). 10. Mode of payment: Online payment - debit card/credit card/net banking/UPI. 11. 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. 12. 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. 13. Data changes: Last date for data changes: Sep 16, 2022, 10:00 AM: All the fields in the Exam form except for the following ones can be changed until the form closes. The following 6 fields can be changed ONLY when there are NO courses in the course cart. And you will be able to edit the following fields only if you: - REMOVE unpaid courses from the cart And/or - CANCEL paid courses 1. Do you come under the SC/ST category? * 2. SC/ST Proof 3. Are you a person with disabilities? * 4. Are you a person with disabilities above 40%? 5. Disabilities Proof 6. What is your role ? Note: Once you remove or cancel a course, you will be able to edit these fields immediately. But, for cancelled courses, refund of fees will be initiated only after 2 weeks. 14. LAST DATE FOR CANCELLING EXAMS and getting a refund: Sep 16, 2022, 10:00 AM 15. 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, NPTEL TEAM

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!

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

Introduction to Machine Learning : Final Feedback Form

Dear student, We are glad that you have attended the NPTEL online certification course. We hope you found the NPTEL Online course useful and have started using NPTEL extensively. In this regard, we would like to have feedback from you regarding our course and whether there are any improvements, you would like to suggest. We are enclosing an online feedback form and would request you to spare some of your valuable time to input your observations. Your esteemed input will help us in serving you better. The link to give your feedback is: https://docs.google.com/forms/d/15gYAWbZsdhuRv_xhXY1DhZRrwN9tPgZ3osGbFxzWgTk/viewform We thank you for your valuable time and feedback. Thanks & Regards, -NPTEL Team

NPTEL April Exams

Dear Leaner,

_NPTEL Team

Introduction to Machine Learning : Assignment 12 Solutions Released!!

Dear Participants, The Solutions of Week 12 for the course "Introduction to Machine Learning" have been released in the portal. Please go through the solution and in case of any doubt post your queries in the forum. Assignment 12 Solution Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=123&lesson=185 Happy Learning! Thanks & Regards, NPTEL Team

Introduction to Machine Learning : Reevaluation!!

Dear Learners, Assignment 7 submission of all students has been reevaluated by making the weightage as 0 question number 4. Assignment 8 submission of all students has been reevaluated by changing the answer for question number 4. Assignment 10 submission of all students has been reevaluated by changing the answer for question number 2. Assignment 11 submission of all students has been reevaluated by making the weightage as 0 question number 3. Students are requested to find their revised scores of Assignments on the Progress page. -NPTEL Team

Hall Tickets for April Exams are released !

If there are any mistakes in the hall ticket such as another person's photo/sign/name, please fill the following Google form & mark the corrections.
GForm Link - https://forms.gle/aqeMCN15MpnTQPpV9 (Deadline - April 22, 2022 at 11.00 AM)
These corrections will be reflected in your e-certificate only.
No changes will be made in the hall ticket.
We will check and verify the same and send an email confirmation.
You will still be allowed to write the exam. We will NOT make any changes in the hall ticket issued to you. Please come to the exam centre with the printout of the same hall ticket, along with a valid govt. approved photo id card.
If the photo/sign/name is yours, we WILL NOT upload any updated photo/sign, etc.
Requests for changes in exam city, exam center, session, or course will NOT be entertained.

Introduction to Machine Learning : Assignment 11 Solutions Released!!

Dear Participants, The Solutions of Week 11 for the course "Introduction to Machine Learning" have been released in the portal. Please go through the solution and in case of any doubt post your queries in the forum. Assignment 11 Solution Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=117&lesson=184 Happy Learning! Thanks & Regards, NPTEL Team

Introduction to Machine Learning : Week 12 Feedback Form

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/1ya5e9i3ZZYT9O-wbvcEvq3o6Fehl26bvj-hDo1-rsrM/viewform Thank you. -NPTEL team

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

Dear Learners, The lecture videos for Week 12 have been uploaded for the course Introduction to Machine Learning . The lectures can be accessed using the following link: Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=123&lesson=124 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- for Week 12 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=123&assessment=146 Assignment-12 for Week 12 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=123&assessment=183 The assignment has to be submitted on or before Wednesday,[20/04/2022], 23:59 IST. As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards, -NPTEL Team

Introduction to Machine Learning : Assignment 10 Solutions Released!!

Dear Participants, The Solutions of Week 10 for the course "Introduction to Machine Learning" have been released in the portal. Please go through the solution and in case of any doubt post your queries in the forum. Assignment 10 Solution Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=109&lesson=180 Happy Learning! Thanks & Regards, NPTEL Team

Introduction to Machine Learning : Week 11 Feedback Form

Introduction to machine learning : week 11 content is live now .

Dear Learners, The lecture videos for Week 11 have been uploaded for the course Introduction to Machine Learning . The lectures can be accessed using the following link: Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=117&lesson=118 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- for Week 11 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=117&assessment=145 Assignment-11 for Week 11 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=117&assessment=181 The assignment has to be submitted on or before Wednesday,[13/04/2022], 23:59 IST. As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards, -NPTEL Team

Introduction to Machine Learning : Assignment 9 Solutions Released!!

Dear Participants, The Solutions of Week 9 for the course "Introduction to Machine Learning" have been released in the portal. Please go through the solution and in case of any doubt post your queries in the forum. Assignment 9 Solution Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=101&lesson=179 Happy Learning! Thanks & Regards, NPTEL Team

Exam Format - April 24, 2022

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

Introduction to Machine Learning : Week 10 Feedback Form

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

Dear Learners, The lecture videos for Week 10 have been uploaded for the course Introduction to Machine Learning . The lectures can be accessed using the following link: Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=109&lesson=110 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- for Week 10 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=109&assessment=144 Assignment-9 for Week 10 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=109&assessment=177 The assignment has to be submitted on or before Wednesday,[06/04/2022], 23:59 IST. As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards, -NPTEL Team

Introduction to Machine Learning : Assignment 8 Solutions Released!!

Dear Participants, The Solutions of Week 8 for the course "Introduction to Machine Learning" have been released in the portal. Please go through the solution and in case of any doubt post your queries in the forum. Assignment 8 Solution Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=93&lesson=178 Happy Learning! Thanks & Regards, NPTEL Team

RBCDSAI Teaching Fellowship Program - REGISTER NOW !!

Introduction to Machine Learning : Week 9 Feedback Form

Introduction to Machine Learning - Assignment 4 and 6 Reevaluation !!

Dear Learner, Submission of all students has been reevaluated by changing the answer for questions: Assignment 4 - Questions 2 and 5 Assignment 6 - Question 6 Students are requested to find their revised scores of Assignments 4 and 6 on the Progress page. -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/noc22_cs29/unit?unit=101&lesson=102 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Practice Assignment-9 for Week-9 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=101&assessment=143 Assignment-9 for Week-9 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=101&assessment=176 The assignment has to be submitted on or before Wednesday,[30/03/2022], 23:59 IST. As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards, -NPTEL Team

Introduction to Machine Learning - Assignment 7 Solutions Released!!

Dear Participants, The Solutions of Week 7 for the course "Introduction to Machine Learning" have been released in the portal. Please go through the solution and in case of any doubt post your queries in the forum. Assignment 7 Solution Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=83&lesson=173 Happy Learning! Thanks & Regards, NPTEL Team

Introduction to Machine Learning : Week 8 Feedback Form

Introduction to machine learning - week-8 content is live now .

Dear Learners, The lecture videos for Week 8 have been uploaded for the course "Introduction to Machine Learning" . The lectures can be accessed using the following link: Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=93&lesson=94 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Practice Assignment-8 for Week-8 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=93&assessment=142 Assignment-8 for Week-8 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=93&assessment=174 The assignment has to be submitted on or before Wednesday,[23/03/2022], 23:59 IST. As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards, -NPTEL Team

Introduction to Machine Learning - Assignment 6 Solutions Released!!

Dear Participants, The Solutions of Week 6 for the course " Introduction to Machine Learning " have been released in the portal. Please go through the solution and in case of any doubt post your queries in the forum. Assignment 6 Solution Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=71&lesson=171 Happy Learning! Thanks & Regards, NPTEL Team

Introduction to Machine Learning : Week 7 Feedback Form

Introduction to machine learning - week-7 content is live now .

Dear Learners, The lecture videos for Week 7 have been uploaded for the course "Introduction to Machine Learning" . The lectures can be accessed using the following link: Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=83&lesson=84 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Practice Assignment-7 for Week-7 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=83&assessment=141 Assignment-7 for Week-7 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=83&assessment=172 The assignment has to be submitted on or before Wednesday,[16/03/2022], 23:59 IST. As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards, -NPTEL Team

Introduction to Machine Learning - Assignment 5 Solutions Released!!

Dear Participants, The Solutions of Week 5 for the course "Introduction to Machine Learning" have been released in the portal. Please go through the solution and in case of any doubt post your queries in the forum. Assignment 5 Solution Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=61&lesson=170 Happy Learning! Thanks & Regards, NPTEL Team

Introduction to Machine Learning : Week 6 Feedback Form

Introduction to machine learning - week-6 content is live now .

Dear Learners, The lecture videos for Week 6 have been uploaded for the course " Introduction to Machine Learning " . The lectures can be accessed using the following link: Link: https://onlinecourses.nptel.ac.in/noc22_cs29/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/noc22_cs29/unit?unit=71&assessment=140 Assignment-6 for Week-6 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=71&assessment=169 The assignment has to be submitted on or before Wednesday,[09/03/2022], 23:59 IST. As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards, -NPTEL Team

Introduction to Machine Learning - Assignment 4 Solutions Released!!

Dear Participants, The Solutions of Week 4 for the course "Introduction to Machine Learning" have been released in the portal. Please go through the solution and in case of any doubt post your queries in the forum. Assignment 4 Solution Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=52&lesson=166 Happy Learning! Thanks & Regards, NPTEL Team

Introduction to Machine Learning : Week 5 Feedback Form

Introduction to machine learning : week 4 feedback form, introduction to machine learning - week-5 content is live now .

Dear Learners, The lecture videos for Week 5 have been uploaded for the course "Introduction to Machine Learning" . The lectures can be accessed using the following link: Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=61&lesson=62 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Practice Assignment-5 for Week-5 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=61&assessment=139 Assignment-5 for Week-5 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=61&assessment=168 The assignment has to be submitted on or before Wednesday,[02/03/2022], 23:59 IST. As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards, -NPTEL Team

Introduction to Machine Learning - Assignment 3 Solutions Released!!

Dear Participants, The Solutions of Week 3 for the course " Introduction to Machine Learning " have been released in the portal. Please go through the solution and in case of any doubt post your queries in the forum. Assignment 3 Solution Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=43&lesson=165 Happy Learning! Thanks & Regards, NPTEL Team

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

Dear Learners, The lecture videos for Week 4 have been uploaded for the course "Introduction to Machine Learning" . The lectures can be accessed using the following link: Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=52&lesson=53 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Practice Assignment-4 for Week-4 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=52&assessment=138 Assignment-4 for Week-4 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=52&assessment=167 The assignment has to be submitted on or before Wednesday,[23/02/2022], 23:59 IST. As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards, -NPTEL Team

Introduction to Machine Learning - Assignment 1 Solutions Released!!

Dear Participants, The Solutions of Week 1 for the course " Introduction to Machine Learning " have been released in the portal. Please go through the solution and in case of any doubt post your queries in the forum. Assignment 1 Solution Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=23&lesson=163 Happy Learning! Thanks & Regards, NPTEL Team

Introduction to Machine Learning - Assignment 2 Solutions Released!!

Dear Participants, The Solutions of Week 2 for the course " Introduction to Machine Learning " have been released in the portal. Please go through the solution and in case of any doubt post your queries in the forum. Assignment 2 Solution Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=33&lesson=164 Happy Learning! Thanks & Regards, NPTEL Team

Assignments for Week 1 & 2 due on Feb 9 2022 !!

Dear Learner, Assignments for Week 1 & 2 are open for submission .The last date for submission is Feb 9 2022 IST 23:59 . If you have not submitted the assignments ,kindly submit the same before the due date. -NPTEL Team.

Introduction to Machine Learning : Week 3 Feedback Form

Introduction to machine learning - week-3 content is live now .

Dear Learners, The lecture videos for Week 3 have been uploaded for the course " Introduction to Machine Learning ". The lectures can be accessed using the following link: Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=43&lesson=44 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Practice Assignment-3 for Week-3 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=43&assessment=137 Assignment-3 for Week-3 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=43&assessment=162 The assignment has to be submitted on or before Wednesday,[16/02/2022], 23:59 IST. As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards, -NPTEL Team

Introduction to Machine Learning : Week 2 Feedback Form

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: Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=33&lesson=34 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/noc22_cs29/unit?unit=33&assessment=136 Assignment-2 for Week-2 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=33&assessment=161 The assignment has to be submitted on or before Wednesday,[09/02/2022], 23:59 IST. As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards, -NPTEL Team

Introduction to Machine Learning : Week 1 Feedback Form

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

Dear Learners, The lecture videos for Week 1 have been uploaded for the course " Introduction to Machine Learning ". The lectures can be accessed using the following link: Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=23&lesson=24 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Practice Assignment-1 for Week-1 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=23&assessment=135 Assignment-1 for Week-1 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=23&assessment=160 The assignment has to be submitted on or before Wednesday,[09/02/2022], 23:59 IST. As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards, -NPTEL Team

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Introduction to Machine Learning : Week 1 content is live now !!

Dear Learners, The lecture videos for Week 1 have been uploaded for the course Introduction to Machine Learning . The lectures can be accessed using the following link: Link: https://onlinecourses.nptel.ac.in/noc22_cs29/unit?unit=23&lesson=24 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Assignment will be released shortly. As we have done so far, please use the discussion forums if you have any questions on this module. Thanks and Regards, -NPTEL Team

Introduction to Machine Learning : Assignment 0 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/noc22_cs29/unit?unit=16&assessment=147 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 2022 courses!

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

Every week, about 2.5 to 4 hours of videos containing content by the Course instructor will be released along with an assignment based on this. Please watch the lectures, follow the course regularly and submit all assessments and assignments before the due date. Your regular participation is vital for learning and doing well in the course. This will be done week on week through the duration of the course.
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NPTEL Introduction to Machine Learning – IITM Assignment 2021

by QuizXp Team
July 28, 2021 October 20, 2021

NPTEL INTRODUCTION TO MACHINE LEARNING – IITM course aimed at helping students enable data-driven disciplines with the increased availability of a variety of data from varied sources There has been increasing attention paid to the various methods of analytics and machine learning.

NPTEL INTRODUCTION TO MACHINE LEARNING is a MOOC course offered by IIT Madras on the NPTEL platform. This course is intend to introduce some of the basic concepts of machine learning The course is developed by Prof. Balaraman Ravindran is currently a Professor in Computer Science at IIT Madras and Mindtree Faculty Fellow.

Who Can Join: This is an elective course. Intended for senior UG/PG students. BE/ME/MS/PhD
Requirements/Prerequisites: We will assume that the students know programming for some of the assignments.If the students have done introductory courses on probability theory and linear algebra it would be helpful. We will review some of the basic topics in the first two weeks as well.
INDUSTRY SUPPORT: Any company in the data analytics/data science/big data domain would value this course.

CRITERIA TO GET A CERTIFICATE

Average assignment score = 25% of the average of the 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

Students will be eligible for CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If any of the 2 criteria are not met, the student will not get the certificate even if the Final score >= 40/100.

NPTEL Introduction to machine learning Assignment Week 12 Answers:-

Q1. In solving a classification problem, if in the learned model there is a large difference between the output of the learned model and the expected output of the learned model over various sources of variability, then we can expect _ the component of the generalisation error to be high.

Q2. Given below are some properties of different classification algorithms. In which among the following would you expect feature

Answer:- A,B,D

Q3. Which of the following measure best analyze the performance of a classifier?

Q4. As discussed in the lecture, most of the classifiers minimize the empirical risk. Which among the following is an exceptional case?

Q5. What do you expect to happen to the variance component of the generalisation error of your model as the size of the training data set increases?

Q6. What happens when your model complexity (such as interaction terms in linear regression, order of polynomial in SVM, etc.) increases?

Answer:- b,c

Q7. Suppose we want an RL agent to learn to play the game of golf. For training purposes, we make use of a golf simulator program. Assume

Q8. You want to toss a fair coin a number of times and obtain the probability of getting heads by taking a simple average. What is the

Q9. You face a particularly challenging RL problem, where the reward distribution keeps changing with time. In order to gain maximum

NPTEL Introduction to machine learning Assignment Week 11 Answers:-

Q1. During parameter estimation for a GMM model using data X, which of the following quantities are you minimizing (directly or indirectly)?

Q2. When executing the Expectation Maximization algorithm, a common problem is the sheer complexity of the number of parameters to estimate. For a typical K-Gaussian Mixture Model in an n-dimensional space, how many independent parameters are being estimated in total?

Q3. Which of the following is an assumption that reduces Gaussian Mixture Models to K-means?

Q4. Given N samples x 1, x 2,…, xN drawn independently from a Gaussian distribution with variance σ 2 and unknown mean μ . Assume that the prior distribution of the mean is also a Gaussian distribution, but with parameters mean μp and variance σ 2 p . Find the MAP estimate of the mean.

Q5. You are presented with a dataset that has hidden/missing variables that influences your data. You are asked to use Expectation Maximization algorithm to best capture the data. How would you define the E and M in Expectation Maximization?

Q6. During parameter estimation for a GMM model using data X, which of the following quantities are you minimizing (directly or indirectly)?

Q7. You are given n p-dimensional data points. The task is to learn a classifier to distinguish between k classes. You come to know that the dataset has missing values. Can you use EM algorithm to fill in the missing values ? (without making any further assumptions)

NPTEL Introduction to machine learning Assignment Week 10 Answers:-

Q1. Considering single-link and complete-link hierarchical clustering, is it possible for a point to be closer to points in other clusters than to points in its own cluster? If so, in which approach will this tend to be observed?

Q2. Consider the following one dimensional data set: 12, 22, 2, 3, 33, 27, 5, 16, 6, 31, 20, 37, 8 and 18. Given k = 3 and initial cluster centers to be 5, 6 and 31, what are the final cluster centres obtained on applying the k -means algorithm?

All the best for the final exam, for extra preparation, take our membership for better score in exam read more here:- Final Exam Membership

Q3. For the previous question, in how many iterations will the k-means algorithm converge?

Q4. In the lecture on the BIRCH algorithm, it is stated that using the number of points N , sum of points SUM and sum of squared points SS , we can determine the centroid and radius of the combination of any two clusters A and B. How do you determine the centroid of the combined cluster? (In terms of N,SUM and SS of both the clusters)

Q5. What assumption does the CURE clustering algorithm make with regards to the shape of the clusters?

Q6. What would be the effect of increasing MinPts in DBSCAN while retaining the same Eps parameter? (Note that more than one statement may be correct)

Q7. Visualize the dataset DS1. Which of the following algorithms will be able to recover the true clusters (first check by visual inspection and then write code to see if the result matches to what you expected).

Q8. For two independent runs of K-Mean clustering is it guaranteed to get same clustering results? Note: seed value is not preserved in independent runs.

Q9. Consider the similarity matrix given below: Which of the following shows the hierarchy of clusters created by the single link clustering algorithm.

Q10. For the similarity matrix given in the previous question, which of the following shows the hierarchy of clusters created by the complete link clustering algorithm.

NPTEL Introduction to machine learning Assignment Week 9 Answers:-

Q1. Consider the bayesian network shown below.

Two students – Manish and Trisha make the following claims:

• Manish claims P(D|{S, L, C}) = P(D|{L, C}) • Trisha claims P(D|{S, L}) = P(D|L)

Q2. Consider the Bayesian graph shown below in Figure 2.

Q3. Using the data given in the previous question, compute the probability of following assignment, P ( i =1, g =1, s =1, l =0) irrespective of the difficulty of the course? (up to 3 decimal places)

Q4. Consider the Bayesian network shown below in Figure 3

• Trisha claims P(H|{S, G, J}) = P(H|{G, J}) • Manish claims P(H|{S, C, J}) = P(H|{C, J})

Q5. Consider the Markov network shown below in Figure 4

Which of the following variables are NOT in the markov blanket of variable “4” shown in the above Figure 4 ? (multiple answers may be correct)

Answer:- d,g

Q6. In the Markov network given in Figure 4, two students make the following claims:

• Manish claims variable “1” is dependent on variable “7” given variable “2”. • Trina claims variable “2” is independent of variable “6” given variable “3”.

Q7. Four random variables are known to follow the given factorization

P ( A 1= a 1, A 2= a 2, A 3= a 3, A 4= a 4)=1 Z ψ 1( a 1, a 2) ψ 2( a 1, a 4) ψ 3( a 1, a 3) ψ 4( a 2, a 4) ψ 5( a 3, a 4)

The corresponding Markov network would be

Q8. Consider the following Markov Random Field.

Which of the following nodes will have no effect on H given the Markov Blanket of H?

Answer:- c,e,f

Q9. Select the correct pairs of (Inference Algorithm, Graphical Model) (note: more than one option may be correct)

Answer:- will update this answers soon and notify on telegram

Q10. Here is a popular toy graphical model. It models the grades obtained by a student in a course and it’s implications. Difficulty represents the difficulty of the course and intelligence is an indicator of how intelligent the student is, SAT represents the SAT scores of the student and Letter presents the event of the student receiving a letter of recommendation from the faculty teaching the course.

Answer:- a,c,d

NPTEL Introduction to machine learning Assignment Week 8 Answers:-

Q1. In a given classification problem, there are 6 different classes. In building a classification model, we want to penalise specific errors made by the model depending upon the actual and predicted class label. For example, given a training data point belonging to class 1, if the model predicts it as class 2, then the penalty for this will be different if for the same data point, the model had predicted it as class 3. To build such a model, we need to select an appropriate

Q2. The Naive Bayes classifier makes the assumption that the ________ are independent given the ________ .

Q3. Consider the problem of learning a function X → Y , where Y is Boolean. X is an input vector ( X 1, X 2), where X 1 is categorical and takes 3 values, and X 2 is a continuous variable (normally distributed). What would be the minimum number of parameters required to define a Naive Bayes model for this function?

Q4. In boosting, the weights of data points that were miscalssified are _________ as training progresses.

Q5. In a random forest model let m << p be the number of randomly selected features that are used to identify the best split at any node of a tree. Which of the following are true? ( p is the original number of features) (Multiple options may be correct)

Q6. Consider the following data for 500 instances of home, 600 instances of office and 700 instances of factory type buildings

Q7. Consider the following graphical model, which of the following are false about the model? (multiple options may be correct)

Answer:- a,b

Q8. Consider the Bayesian network given in the previous question. Let ‘A’, ‘B’, ‘C’, ‘D’and ‘E’denote the random variables shown in the network. Which of the following can be inferred from the network structure?

NPTEL Introduction to machine learning Assignment Week 7 Answers:-

Q1. For the given confusion matrix, compute the recall

Q2. Which of the following are true? TP – True Positive, TN – True Negative, FP – False Positive, FN – False Negative

Answer:- a,c

Q3. How does bagging help in improving the classification performance?

Q4. Which method among bagging and stacking should be chosen in case of limited training data? and what is the appropriate reason for your preference?

Q5. Which of the following statements are false when comparing Committee Machines and Stacking

Q6. Which of the following measure best analyze the performance of a classifier?

Q7. For the ROC curve of True positive rate vs False positive rate, which of the following are true?

Q8. Which of the following are true about using 5-fold cross validation with a data set of size n = 100 to select the value of k in the kNN algorithm.

NPTEL Introduction to machine learning Assignment Week 6 Answers:-

Q1. Decision trees can be used for __________ .

Q2. In building a decision tree model, to control the size of the tree, we need to control the number of regions. One approach to do this would be to split tree nodes only if the resultant decrease in the sum of squares error exceeds some threshold. For the described method, which among the following are true?

Q3. In a decision tree, if we decide to swap out the usual splits (of the form xi < k or xi > k ) and instead used a linear combination of features instead, (like βTX + β 0 ), where the parameters of the hyperplane β , β 0 are also simultaneously learnt, which of the following statements would be true?

Answer:- b,d

Q4. Having built a decision tree, we are using reduced error pruning to reduce the size of the tree. We select a node to collapse. For this particular node, on the left branch, there are 3 training data points with the following outputs: 5, 7, 9.6 and for the right branch, there are four training data points with the following outputs: 8.7, 9.8, 10.5, 11. The average value of the outputs of data points denotes the response of a branch. The original responses for data points along the two branches (left right respectively) were response _ left and, response _ right and the new response after collapsing the node is response _ new . What are the values for response _ left , response _ right and response _ new (numbers in the option are given in the same order)?

Q5. Which among the following split-points for the feature 1 would give the best split according to the information gain measure?

Q6. For the same dataset, which among the following split-points for feature2 would give the best split according to the gini index measure?

Q7. In which of the following situations is it appropriate to introduce a new category ’Missing’ for missing values? (multiple options may be correct)

Answer:- a,d

NPTEL Introduction to machine learning Assignment Week 5 Answers:-

Q1. You are given the N samples of input (x) and output (y) as shown in the figure below. What will be the most appropriate model y = f ( x )

Q2. Given N samples x 1, x 2,…, xN drawn independently from a Gaussian distribution with variance σ 2 and unknown mean μ , find the MLE of the mean.

Q3. Consider the following function.

Q4. 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

Answer:- WILL BE UPDATED BY MIDNIGHT AND WILL NOTIFY ON TELEGRAM , CLICK ON BELOW IMAGE FOR LINK

Q5. Which of the following statements are true:

Answer:- B,C

Q6. We have a function which takes a two-dimensional input x =( x 1, x 2) and has two parameters w =( w 1, w 2) given by f ( x , w )= σ ( σ ( x 1 w 1) w 2+ x 2) where σ ( x )=11+ e − x .We use backpropagation to estimate the right parameter values. We start by setting both the parameters to 2. Assume that we are given a training point x 2=1, x 1=0, y =3. Given this information answer the next two questions. What is the value of ∂ f ∂ w 2.

Q7. If the learning rate is 0.5, what will be the value of w 2 after one update using backpropagation algorithm?

Q8. Which of the following are true when comparing ANNs and SVMs?

Q9. Which of the following are correct?

Q10. Which of the following are false?

NPTEL Introduction to machine learning Assignment Week 4 Answers:-

Q1. Suppose we use a linear kernel SVM to build a classifier for a 2-class problem where the training data points are linearly separable. In general, will the classifier trained in this manner produce the same decision boundary as the classifier trained using the perceptron training algorithm on the same training data?

Q2. Consider the data set given below. Claim: PLA (perceptron learning algorithm) can be used to learn a classifier that achieves zero misclassification error on the training data. This claim is:

Q3. For a support vector machine model, let xi be an input instance with label yi . If yi ( β ^0+ xTiβ ^)>1 where β 0 and β ^ are the estimated parameters of the model, then

Q4. Suppose we use a linear kernel SVM to build a classifier for a 2-class problem where the training data points are linearly separable. In general, will the classifier trained in this manner be always the same as the classifier trained using the perceptron training algorithm on the same training data?

Q5. Train a linear regression model (without regularization) on the above dataset.Report the coefficients of the best fit model. Report the coefficients in the following format: β 0 β 1 β 2 β 3.

Q6. Train an l2 regularized linear regression model on the above dataset. Vary the regularization parameter from 1 to 10. As you increase the regularization parameter, absolute value of the coefficients (excluding the intercept) of the model:

Q7. Train an l 2 regularized logistic regression classifier on the modified iris dataset. We recommend using sklearn. Use only the first two features for your model. We encourage you to explore the impact of varying different hyperparameters of the model. Kindly note that the C parameter mentioned below is the inverse of the regularization parameter λ . As part of the assignment train a model with the following hyperparameters: Model: logistic regression with one-vs-rest classifier, C =1 e 4 For the above set of hyperparameters, report the best classification accuracy

Q8. Train an SVM classifier on the modified iris dataset. We recommend using sklearn. Use only the first two features for your model. We encourage you to explore the impact of varying different hyperparameters of the model. Specifically try different kernels and the associated hyperparameters. As part of the assignment train models with the following set of hyperparameters RBF-kernel, gamma = 0.5, one-vs-rest classifier, no-feature-normalization. Try C = 0.01, 1, 10. For the above set of hyperparameters, report the best classification accuracy along with total number of support vectors on the test data.

NPTEL Introduction to machine learning Assignment Week 3 Answers:-

Q1. Consider the case where two classes follow Gaussian distribution which are centered at (4, 7) and (−4, −1) and have identity covariance matrix. Which of the following is the separating decision boundary using LDA assuming the priors to be equal?

Q2. Consider the following data with two classes. The color indicates different class.

Q3. We discussed the use of MLE for the estimation of parameters of logistic regression model. We used which of the following assumptions to derive the likelihood function ?

Q4. Which of the following statements is true about LDA regarding outliers?

Q5. Consider the following distribution of training data:

Q6. Suppose that we have two variables, X and Y (the dependent variable). We wish to find the relation between them. An expert tells us that relation between the two has the form Y = m log( X )+ c . Available to us are samples of the variables X and Y . Is it possible to apply linear regression to this data to estimate the values of m and c ?

Q7. In a binary classification scenario where x is the independent variable and y is the dependent variable, logistic regression assumes that the conditional distribution y | x follows a

Q8. Assuming that you apply LDA to this data, what is the estimated covariance matrix?

Answer:- F (THIS MIGHT BE WRONG PLEASE CHECK AT YOUR LEVEL)

Q9. Given the following 3D input data, identify the principal component. (Steps: center the data, calculate the sample covariance matrix, calculate the eigenvectors and eigenvalues, identify the principal component)

Answer:- B (THIS MIGHT BE WRONG PLEASE CHECK AT YOUR LEVEL)

Q10. For the data given in the previous question, find the transformed input along the first two principal components.

NPTEL Introduction to machine learning Assignment Week 2 Answers:-

Q1. Given a training dataset, the following visualization shows the fit of three different models (in blue line). Assume that the test data and training data come from the same distribution. What can you conclude from the following visualizations? Multiple options can be correct.

Answer:- A,C,D

Q2. Suppose you have fitted a complex regression model on a dataset. Now, you are using Ridge regression with tuning parameter lambda to reduce its complexity. Choose the option below which describes relationship of bias and variance with lambda.

Q3. Given a training data set of 10,000 instances, with each input instance having 17 dimensions and each output instance having 2 dimensions, the dimensions of the design matrix used in applying linear regression to this data is

Q4. Suppose we want to add a regularizer to the linear regression loss function, to control the magnitudes of the weights β . We have a choice between Ω1( β )=∑ i =1 p | β | and Ω2( β )=∑ i =1 pβ 2. Which one is more likely to result in sparse weights?

Q5. Consider forward selection, backward selection and best subset selection with respect to the same data set. Which of the following is true?

Q6. In the formulation of the method, we observe that in iteration k , we regress the entire dataset on z 0, z 1,… zk −1 . It seems like a waste of computation to recompute the coefficients for z 0 a total of p times, z 1 a total of p −1 times and so on. Can we re-use the coefficients computed in iteration j for iteration j +1 for zj −1 ?

Q7. Consider the following five training examples We want to learn a function f ( x ) of the form f ( x )= ax + b which is parameterised by ( a , b ). Using squared error as the loss function, which of the following parameters would you use to model this function to get a solution with the minimum loss.

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

NPTEL Introduction to machine learning Assignment Week 1 Answers:-

Q1 . Which of the following is a supervised learning problem?

Answer:- B,C,D

Q2 – Which of the following is not a classification problem?

Answer:- A,C

Q3 – Which of the following is a regression task? (multiple options may be correct)

Note:- WE NEVER PROMOTE COPYING AND 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 to reference, so we urge do you assignment on your own.

Also Check:- INTERNSHIP OPPORTUNITIES

Q4 – Which of the following is an unsupervised task?

Answer:- C,D

Q5 – Which of the following is a categorical feature?

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)

Answer:- F – 5/8

NOTE:- IF THERE IS ANY CHANGE IN ANSWERS OF NPTEL Introduction to Machine Learning WILL UPDATE BEFORE LAST DATE AND NOTIFY ON TELEGRAM OR WHATSAPP. SO KINDLY JOIN US, CLICK ON BELOW IMAGE AND JOIN US.

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

Answer:-E -3+ ı 7–√,3− ı 7–√where ı =−1

Q8 – What happens when your model complexity increases? (multiple options may be correct)

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?

Answer:- G – 0.181

Q10 – Which of the following are false about bias and variance of overfitted and underfitted models? (multiple options may be correct)

NPTEL Introduction to machine learning Assignment Week 0 Answers:-

Q1. There are n bins of which the k -th bin contains k −1 blue balls and n − k red balls. You pick a bin at random and remove two balls at random without replacement. Find the probability that:

Answer:- C – 1/2,2/3

Q2. A medical company touts its new test for a certain genetic disorder. The false negative rate is small: if you have the disorder, the probability that the test returns a positive result is 0.999. The false positive rate is also small: if you do not have the disorder, the probability that the test returns a positive result is only 0.005. Assume that 2% of the population has the disorder. If a person chosen uniformly from the population is tested and the result comes back positive, what is the probability that the person has the disorder?

Answer:- A – 0.803

Q3. In an experiment, n coins are tossed, with each one showing up heads with probability p independently of the others. Each of the coins which shows up heads is then tossed again. What is the probability of observing 5 heads in the second round of tosses, if we toss 15 coins in the first round and p = 0.4?

Answer:- B – 0.055

Q4. Consider two random variables X and Y having joint density function f ( x , y )=2 e − x − y ,< x < y <∞. Are X and Y independent? Find the covariance of X and Y .

Answer:- A – Yes, 1/4

Q5. An airline knows that 5 percent of the people making reservations on a certain flight will not show up. Consequently, their policy is to sell 52 tickets for a flight that can hold only 50 passengers. What is the probability that there will be a seat available for every passenger who shows up?

Answer:- D – 0.7405

Q6. Let X have mass function f ( x )={{ x ( x +1)}−10if x =1,2,…,otherwise,

Answer:- B – α <1

Q7. Is the following a distribution function?

Answer:- A – Yes, x −2 e −1/ x , x >0

Q8. Can the value of a probability density function be greater than one? What about the cumu- lative distribution function?

Answer:- B – PDF: yes, CDF: no

Q9. You are given a biased coin with probability of seeing a head is p = 0.6 and probability of seeing a tail is q = 0.4. Suppose you toss the coin 10 times, what is the probability of you getting the head at most 2 times? Also, what is the probability of you getting the head for the first time on your fourth attempt?

Answer:- A – 0.012, 0.038

Q10. Given a bag containing 6 red balls, 4 blue balls and 7 green balls, what is the probability that in 5 trials, at least 3 red balls are drawn from the bag?

Answer:- A – 0.24

Q11. In the experiment from the previous question, what is the probability of picking a red ball for the first time on the fourth attempt?

Answer:- C – 0.096

Deep Learning | Week 12

Course Name: Deep Learning

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These are NPTEL Deep Learning Week 12 Assignment 12 Answers

Q1. When the dice coefficient between two samples will be one? a. When there is a perfect overlap between the two samples. b. When there is no overlap between the two samples. c. It cannot be one. d. If the inner product of two samples is one.

Answer: a. When there is a perfect overlap between the two samples.

Q2. There are two distributions. The first distribution, P is a uniform distribution between [-1, 1]. Another distribution, Q is a Normal distribution. What will be the KL(Q| |P)? a. 0.5 b. 0.0 c. 10 d. infinity

Answer: d. infinity

Q3. Which of the following is True regarding the reconstruction loss (realized as mean squared error between input and predicted signal) of standard auto-encoder? a. Such loss is not differentiable and cannot be used for back propagation b. Such loss tends to form distinct clusters in latent space c. Such loss cannot be optimized with gradient descent d. None of the above

Answer: b. Such loss tends to form distinct clusters in latent space

Q4. For an auto-encoder, suppose we give an input signal, x and reconstruct a signal y. Which one of the following objective functions can be MINIMIZED to train the parameters of the auto encoder using gradient descent optimizer? a. L(x, y) = exp -(lx-yl) b. L(x,y)= -log(lx-yl) c. L(x, y) = exp(|x-yl) d. L(x,y) = (x + y)²

Answer: c. L(x, y) = exp(|x-yl)

Q5. Suppose we have a 2N dimensional Normal distribution in which we assume all components are independent of each other. What will be the size (number of elements) of the vector to fully represent the covariance matrix of this distribution? a. N b. 2N c N/2 d. N/4

Answer: b. 2N

Q6. What will happen if we do not enforce KL divergence loss in VAE latent code space? a. The latent code distribution will be mimic zero mean and unit variance Normal distribution b. Network will learn to form distinctive clusters with high standard deviation for each cluster c. Network will learn to form distinctive clusters with low standard deviation for each cluster d. None of the above

Answer: c. Network will learn to form distinctive clusters with low standard deviation for each cluster

Q7. Figure shows latent vector addition of two concepts of “man without a hat” and “hat”. What is expected from the resultant vector?

a. Hat without man b. Man with hat c. Woman with hat d. Woman without hat

Answer: b. Man with hat

Q8. Which one of the following statements is True in the original GAN training? a. It is desired that the Discriminator loss monotonically goes down b. It is desired that the Generator loss monotonically goes down c. It is desired that the Discriminator loss monotonically goes down while the Discriminator loss monotonically goes up d. It is desired that neither of the losses of Discriminator or Generator monotonically goes up or down monotonically

Answer: d. It is desired that neither of the losses of Discriminator or Generator monotonically goes up or down monotonically

Q9. Which one of the following statements is true about Variational Autoencoder (VAE)? a. VAE can only be applied on monochrome images b. VAE reconstructions tend to be blurry c. VAE reconstructions always have high frequency preserving details d. VAE latent space is designed to be NOT smooth

Answer: b. VAE reconstructions tend to be blurry

Q10. Suppose we have trained a Variational Auto-encoder (VAE) on faces with 4D latent code and after convergence, the mean vector and standard deviation vector is given by [2.1, -1.9, 3.8, 0.9] and [0.5, 0.8, 0.6, 0.2] respectively. Now, in evaluation phase, we pass a new face image (belonging to the same domain as those used in training phase). Which of the following latent code is most probable to be encountered when we pass this new face image? a. [1.0,3.9,2.9,3.2] b. [2.0,-1.92,3.8,0.92] c [2.1,23,-39,10] d. [3.2,-3.6,09,12]

Answer: b. [2.0,-1.92,3.8,0.92]

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Introduction to Machine Learning NPTEL Week 3 Solutions NPTEL 2023

This set of MCQ(multiple choice questions) focuses on the Introduction to Machine Learning NPTEL Week 3 Solutions NPTEL 2023 .

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Introduction to Machine learning NPTEL 2023 Week 3 Solutions

Q1. Fill in the blanks: K-Nearest Neighbor is a _______, ________ algorithm

a) Non-parametric, eager b) Parametric, eager c) Non-parametric, lazy d) Parametric, lazy

Q2. You have been given the following 2 statements. Find out which of these options is/are true in the case of k-NN. (i) In case of very large value of k, we may include points from other classes in to the neighborhood. (ii) In case of too small value of k, the algorithm is very sensitive to noise.

a) (i) is True and (ii) is False b) (i) is False and (ii) is True c) Both are True d) Both are False

Q3. State whether the statement is True/False: k-NN algorithm does more computation on test time rather than train time.

a) True b) False

Introduction to Machine Learning NPTEL Week 3 Solutions

Q4. Suppose you are given the following images(1 represents the left image, 2 represents the middle and 3 represents the right). Now you task is to find out the value of k in k-NN in each of the images shown below. Here k1 is for 1st, k2 is for 2nd and k3 is for 3rd figure.

a) k1 > k2 > k3 b) k1 < k2 > k3 c) k1 < k2 < k3 d) None of these

Q5. Which of the following necessitates feature reduction in machine learning?

a) Irrelevant and redundant features b) Limited training data c) Limited computational resources d) All of the above

Q6. Suppose, you have given the following data where x and y are the 2 input variables and Class is the dependent variable.

a) + Class b) – Class c) Can’t say d) None of these

Q7. What is the optimum number of principal components in the below figure?

a) 10 b) 20 c) 30 d) 40

Q8. Suppose we are using dimensionality reduction as pre-processing technique, i.e, instead of using all the features, we reduce the data to k dimensions with PCA. And then use these PCA projections as our features. Which of the following statements is correct? Choose which of the options is correct?

a) Higher value of ‘k’ means more regularization b) Higher value of ‘k’ means less regularization

Q9. In collaborative filtering-based recommendation, the items are recommended based on:

a) Similar users b) Similar items c) Both of the above d) None of the above

Q10. The major limitation of collaborative filtering is:

a) Cold start b) Overspecialization c) None of the above

Q11. Consider the figures below. Which figure shows the most probable PCA component directions for the data points?

a) A b) B c) C d) D

Q12. Suppose that you wish to reduce the number of dimensions of a given data to dimensions using PCA. Which of the following statement is correct?

a) Higher means more regularization b) Higher means less regularization c) Can’t say

Q13. Suppose you are given 7 plots 1-7 (left to right) and you want to compare Pearson correlation coefficients between variables of each plot. Which fo the following is true? 1. 1 < 2<3<4 2. 1>2>3>4 3. 7<6<5<4 4. 7>6>5>4

a) 1 and 3 b) 2 and 3 c) 1 and 4 d) 2 and 4

Q14. Imagine you are dealing with 20 class classification problem. What is the maximum number of discriminant vectors that can be produced by LDA?

a) 20 b) 19 c) 21 d) 10

Q15. In which of the following situations collaborative filtering algorithm is appropriate?

a) You manage an online bookstore and you have the book ratins from many users. For each user, you want to recommend other books he/she will like based on her previous ratins and other users’ ratings. b) You manage an online bookstore and you have the book raings from many users. You want to predict the expected sales volume(No of books sold) as a function of average rating of a book. c) Both A and B d) None of the above

Q1. Which of the following is false about a logistic regression based classifier?

a) The logistic function is non-linear in the weights b) The logistic function is linear in the weights c) he decision boundary is non-linear in the weights d) The decision boundary is linear in the weights

Answer: a,c

Q2. Consider the case where two classes follow Gaussian distribution which are centered at (3, 9) and (−3, 3) and have identity covariance matrix. Which of the following is the separating decision boundary using LDA assuming the priors to be equal?

a) y−x=3 b) x+y=3 c) x+y=6 d) both (b) and (c) e) None of the above f) Can not be found from the given information

Q3. Consider the following relation between a dependent variable and an independent variable identified by doing simple linear regression. Which among the following relations between the two variables does the graph indicate?

a) as the independent variable increases, so does the dependent variable b) as the independent variable increases, the dependent variable decreases c) if an increase in the value of the dependent variable is observed, then the independent variable will show a corresponding increase d) if an increase in the value of the dependent variable is observed, then the independent variable will show a corresponding decrease e) the dependent variable in this graph does not actually depend on the independent variable f) none of the above

Q4. Given the following distribution of data points:

What method would you choose to perform Dimensionality Reduction?

a) Linear Discriminant Analysis b) Principal Component Analysis

Q5. In general, which of the following classification methods is the most resistant to gross outliers?

a) Quadratic Discriminant Analysis (QDA) b) Linear Regression c) Logistic regression d) Linear Discriminant Analysis (LDA)

Q6. Suppose that we have two variables, X and Y (the dependent variable). We wish to find the relation between them. An expert tells us that relation between the two has the form Y=m+X2+c=+2+. Available to us are samples of the variables X and Y. Is it possible to apply linear regression to this data to estimate the values of m and c?

a) no b) yes c) insufficient information

Q7. In a binary classification scenario where x is the independent variable and y is the dependent variable, logistic regression assumes that the conditional distribution y|x| follows a

a) Bernoulli distribution b) binomial distribution c) normal distribution d) exponential distribution

Q8. Consider the following data:

Assuming that you apply LDA to this data, what is the estimated covariance matrix?

a) [1.8750.31250.31250.9375][1.8750.31250.31250.9375] b) [2.50.41670.41671.25] c) [1.8750.31250.31251.2188] d) [2.50.41670.41671.625] e) [3.251.16671.16672.375] f) [2.43750.8750.8751.7812] g) None of these

Q9. Given the following 3D input data, identify the principal component.

(Steps: center the data, calculate the sample covariance matrix, calculate the eigenvectors and eigenvalues, identify the principal component)

a) ⎢−0.10220.00180.9948⎤⎦⎥ b) ⎡⎣⎢0.5742−0.81640.0605⎤⎦⎥ c) ⎢0.57420.81640.0605⎤⎦⎥ d) ⎡⎣⎢−0.57420.81640.0605⎤⎦ e) ⎡⎣⎢0.81230.57740.0824⎤⎦⎥ f) None of the above

Q10. For the data given in the previous question, find the transformed input along the first two principal components.

a) ⎡⎣⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢0.6100−0.4487−1.26511.33450.5474−1.0250−1.26721.5142−0.0196−0.1181−0.11630.5702−0.72570.27270.1724−0.0355⎤⎦⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥ b) ⎡⎣⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢−0.1817−1.2404−2.05680.5428−0.2443−1.8167−2.05890.72250.89440.79590.79771.48420.18841.18681.08640.8785⎤⎦⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥ c) ⎡⎣⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢−6.2814−4.3143−3.7368−1.79502.29173.52894.91865.38830.6100−0.4487−1.26511.33450.5474−1.0250−1.26721.5142⎤⎦⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥ d) ⎡⎣⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢1.47213.43924.01665.958410.045111.282312.672013.1418−0.1817−1.2404−2.05680.5428−0.2443−1.8167−2.05890.7225⎤⎦⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥ e) None of the above

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Introduction to Machine Learning NPTEL Week 3 Solutions. Q6. Suppose that we have two variables, X and Y (the dependent variable). We wish to find the relation between them. An expert tells us that. relation between the two has the form Y=m+X2+c=+2+. Available to us are samples of the variables X and Y.
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