Statistical Learning for Reliability Analysis

By Prof. Monalisa Sarma | IIT Kharagpur

Learners enrolled: 1921 | Exam registration: 441

ABOUT THE COURSE:

Statistical Learning (SL) is the science of analyzing data to convert information to useful knowledge. This knowledge could help us understand our world better, and in many contexts enable us to make better decisions. While this is the broad and grand objective, there is a huge demand to solve many problems with computationally intelligent techniques, such ML, DL, AI and SL. This course seeks to present the participants a wide range of statistical learning approaches related to data sampling, hypothesis testing, statistical inference with both parametric and non-parametric methods, dealing data with one or more population, variance analysis, t-testing, likelihood estimation, etc.

INTENDED AUDIENCE: The course is of interdisciplinary nature and students from CSE, IT, EE, ECE, CE, ME, etc. can take this course.

PREREQUISITES: This course requires that the students are familiar with high-school level linear algebra, calculus, probability and statistics.

INDUSTRY SUPPORT: All IT companies, in general.

Summary

Course Status :	Completed
Course Type :	Elective
Language for course content :	English
Duration :	12 weeks
Category :	Computer Science and Engineering
Credit Points :	3
Level :	Undergraduate/Postgraduate
Start Date :	22 Jul 2024
End Date :	11 Oct 2024
Enrollment Ends :	05 Aug 2024
Exam Registration Ends :	16 Aug 2024
Exam Date :	27 Oct 2024 IST

Note: This exam date is subject to change based on seat availability. You can check final exam date on your hall ticket.

Page Visits

Course layout

Week 1: Introduction to reliability, reliability estimation, concept of statistical learning, advanced techniques to reliability analysis.

Week 2: Probability distribution techniques: discrete and continuous probability distributions and their applications to reliability estimation modeling.

Week 3: Sampling distribution techniques and their different applications for reliability prediction.

Week 4: Statistical inference technique-I (Parametric-based approaches: Hypothesis testing, Confidence interval estimation).

Week 5: Case studies for reliability analysis with parametric-based approaches.

Week 6: Statistical inference techniques-II (Non-parametric-based approaches: Correlation analysis, Relation analysis, Regression analysis).

Week 7: Case studies for reliability analysis with non-parametricbased approaches

Week 8: Statistical learning with single population, pair t-tests techniques. Illustration with applications to reliability analysis.

Week 9: Statistical learning with more than one population, ANOVA techniques. Illustration with applications to reliability analysis.

Week 10: Maximum likelihood estimation techniques. Illustration with applications to reliability analysis.

Week 11: Statistical method of data classification. Illustration with applications to reliability analysis.

Week 12: Entropy and its applications to statistical learning. Illustration with applications to reliability analysis.

Books and references

An Introduction to Statistical Learning: with Applications in R, G James, D. Witten, T Hastie, and R. Tibshirani, (Springer)
Software for Data Analysis: Programming with R (Statistics and Computing), John M. Chambers (Springer)
Advances in Complex Data Modeling and Computational Methods in Statistics, Anna Maria Paganoni and Piercesare Secchi, (Springer)

Instructor bio

Prof. Monalisa Sarma

IIT Kharagpur

Prof. Monalisa Sarma received her Ph.D. degree in Computer Science & Engineering from Indian Institute of Technology Kharagpur, India. She holds M.S. (by research) and B. Tech. degrees both in Computer Science & Engineering from Indian Institute of Technology Kharagpur, India, and North Hill University, India, respectively. Presently, she is an Assistant Professor, Subir Chowdhuri School of Quality and Reliability, India Institute of Technology Kharagpur. Prior to joining Indian Institute of Technology Kharagpur, she was working in the Department of Computer Science & Engineering, Indian Institute of Technology Indore and Siemens Research and Devolvement, Bangalore, India. Her current research includes human reliability, big data security, biometric-based cryptography, etc.

Course certificate

The course is free to enroll and learn from. But if you want a certificate, you have to register and write the proctored exam conducted by us in person at any of the designated exam centres.
The exam is optional for a fee of Rs 1000/- (Rupees one thousand only).
Date and Time of Exams: 27 October 2024 Morning session 9am to 12 noon; Afternoon Session 2pm to 5pm.
Registration url: Announcements will be made when the registration form is open for registrations.
The online registration form has to be filled and the certification exam fee needs to be paid. More details will be made available when the exam registration form is published. If there are any changes, it will be mentioned then.
Please check the form for more details on the cities where the exams will be held, the conditions you agree to when you fill the form etc.

CRITERIA TO GET A CERTIFICATE

Average assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.
Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

Certificate will have your name, photograph and the score in the final exam with the breakup.It will have the logos of NPTEL and IIT Kharagpur .It will be e-verifiable at nptel.ac.in/noc.

Only the e-certificate will be made available. Hard copies will not be dispatched.

Once again, thanks for your interest in our online courses and certification. Happy learning.

- NPTEL team

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