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Course10610523960 lectures

Statistical Learning for Reliability Analysis

Prof. Monalisa Sarma · IIT Kharagpur

Start with lecture 1 At NPTEL

About

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.

Lectures

Week 1

  1. Lecture 01 : Introduction to Reliability Engineering YouTube
  2. Lecture 02 : Introduction to Statistical Methods in Reliability YouTube
  3. Lecture 03 : Concept of Probability and Probability Theory YouTube
  4. Lecture 04 : Tutorial on Introduction to RE, SL and Probability Theory (Part-I) YouTube
  5. Lecture 05 : Conditional, Total and Reverse Probability YouTube

Week 2

  1. Lecture 06 : Tutorial on Conditional Probability and Total Probability YouTube
  2. Lecture 07 : Introduction to Probability Distributions YouTube
  3. Lecture 08 : Introduction to Probability Distributions (Contd.) YouTube
  4. Lecture 09 : Discrete Probability Distribution (Part 1) YouTube
  5. Lecture 10 : Discrete Probability Distribution (Part 2) YouTube

Week 3

  1. Lecture 11 : Tutorial on Discrete Probability Distributions YouTube
  2. Lecture 12 : Continuous Probability Distributions (Part 1) YouTube
  3. Lecture 13 : Continuous Probability Distributions (Part 2) YouTube
  4. Lecture 14 : Tutorial on Continuous Probability Distribution Functions (Part-1) YouTube
  5. Lecture 15 : Tutorial on Continuous Probability Distribution Functions (Part 2) YouTube

Week 4

  1. Lecture 16 : Sampling Distributions (Part 1) YouTube
  2. Lecture 17 : Sampling Distributions (Part 2) YouTube
  3. Lecture 18 : Sampling Distributions (Part 3) YouTube
  4. Lecture 19 : Sampling Distributions (Part 4) YouTube
  5. Lecture 20 : Sampling Distributions (Part 5) YouTube

Week 5

  1. Lecture 21 : Tutorial on Sampling Distributions YouTube
  2. Lecture 22 : Statistical Inference (Part 1) YouTube
  3. Lecture 23 : Statistical Inference (Part 2) YouTube
  4. Lecture 24 : Statistical Inference (Part 3) YouTube
  5. Lecture 25 : Tutorial on Statistical Inference YouTube

Week 6

  1. Lecture 26 : Statistical Inference (Part 4) YouTube
  2. Lecture 27 : Statistical Inference (Part 5) YouTube
  3. Lecture 28 : Tutorial on Confidence Interval YouTube
  4. Lecture 29 : Statistical Inference (Part 6) YouTube
  5. Lecture 30 : Statistical Inference (Part 7) YouTube

Week 7

  1. Lecture 31 : Statistical Inference (Part 8) YouTube
  2. Lecture 32 : ANOVA-I YouTube
  3. Lecture 33 : ANOVA-II YouTube
  4. Lecture 34 : ANOVA – III YouTube
  5. Lecture 35 : ANOVA – IV YouTube

Week 8

  1. Lecture 36 : ANOVA – V YouTube
  2. Lecture 37 : ANOVA – VI YouTube
  3. Lecture 38 : Correlation Analysis (Part-I) YouTube
  4. Lecture 39 : Correlation Analysis (Part-II) YouTube
  5. Lecture 40 : Regression Analysis (Part-I) YouTube

Week 9

  1. Lecture 41 : Regression Analysis (Part-II) YouTube
  2. Lecture 42 : Regression Analysis (Part-III) YouTube
  3. Lecture 43 : Tutorial on Relation Analysis YouTube
  4. Lecture 44 : Auto-Regression Analysis YouTube
  5. Lecture 45 : Logistic Regression (Part-I) YouTube

Week 10

  1. Lecture 46 : Logistic Regression (Part-II) YouTube
  2. Lecture 47 : Logistic Regression (Part-III) YouTube
  3. Lecture 48 :Tutorial on Logistic Regression YouTube
  4. Lecture 49 : Introduction YouTube
  5. Lecture 50 : Bayes Classification (Part-I) YouTube

Week 11

  1. Lecture 51 : Bayesian Classification (Part—II) YouTube
  2. Lecture 52 : k-Nearest Neighbor Classification YouTube
  3. Lecture 53 : Tutorial on Classification Techniques YouTube
  4. Lecture 54 : Support Vector Machine (Part-I) YouTube
  5. Lecture 55 : Support Vector Machine (Part-II) YouTube

Week 12

  1. Lecture 56 : SVM (Part-III) YouTube
  2. Lecture 57 : SVM (Part-IV) YouTube
  3. Lecture 58 : SVM (Part-V) YouTube
  4. Lecture 59 : SVM (Part-VI) YouTube
  5. Lecture 60 : Tutorial on SVM YouTube

Syllabus and materials

At NPTEL, as the course publishes them.

Credits and licence

Institution
IIT Kharagpur
Department
Computer Science and Engineering
Teacher
Prof. Monalisa Sarma
Level
Undergraduate
Language
English
Subjects
Computer science, Engineering
Source
NPTEL
Licence
CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
Attribution
NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
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