Learn · Statistical Learning for Reliability Analysis
Lecture 3 of 60Week 1
Lecture 03 : Concept of Probability and Probability Theory
Prof. Monalisa Sarma · IIT Kharagpur
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- Statistical Learning for Reliability Analysis, IIT Kharagpur (at NPTEL)
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All the lectures
- Lecture 01 : Introduction to Reliability Engineering
- Lecture 02 : Introduction to Statistical Methods in Reliability
- Lecture 03 : Concept of Probability and Probability Theory
- Lecture 04 : Tutorial on Introduction to RE, SL and Probability Theory (Part-I)
- Lecture 05 : Conditional, Total and Reverse Probability
- Lecture 06 : Tutorial on Conditional Probability and Total Probability
- Lecture 07 : Introduction to Probability Distributions
- Lecture 08 : Introduction to Probability Distributions (Contd.)
- Lecture 09 : Discrete Probability Distribution (Part 1)
- Lecture 10 : Discrete Probability Distribution (Part 2)
- Lecture 11 : Tutorial on Discrete Probability Distributions
- Lecture 12 : Continuous Probability Distributions (Part 1)
- Lecture 13 : Continuous Probability Distributions (Part 2)
- Lecture 14 : Tutorial on Continuous Probability Distribution Functions (Part-1)
- Lecture 15 : Tutorial on Continuous Probability Distribution Functions (Part 2)
- Lecture 16 : Sampling Distributions (Part 1)
- Lecture 17 : Sampling Distributions (Part 2)
- Lecture 18 : Sampling Distributions (Part 3)
- Lecture 19 : Sampling Distributions (Part 4)
- Lecture 20 : Sampling Distributions (Part 5)
- Lecture 21 : Tutorial on Sampling Distributions
- Lecture 22 : Statistical Inference (Part 1)
- Lecture 23 : Statistical Inference (Part 2)
- Lecture 24 : Statistical Inference (Part 3)
- Lecture 25 : Tutorial on Statistical Inference
- Lecture 26 : Statistical Inference (Part 4)
- Lecture 27 : Statistical Inference (Part 5)
- Lecture 28 : Tutorial on Confidence Interval
- Lecture 29 : Statistical Inference (Part 6)
- Lecture 30 : Statistical Inference (Part 7)
- Lecture 31 : Statistical Inference (Part 8)
- Lecture 32 : ANOVA-I
- Lecture 33 : ANOVA-II
- Lecture 34 : ANOVA – III
- Lecture 35 : ANOVA – IV
- Lecture 36 : ANOVA – V
- Lecture 37 : ANOVA – VI
- Lecture 38 : Correlation Analysis (Part-I)
- Lecture 39 : Correlation Analysis (Part-II)
- Lecture 40 : Regression Analysis (Part-I)
- Lecture 41 : Regression Analysis (Part-II)
- Lecture 42 : Regression Analysis (Part-III)
- Lecture 43 : Tutorial on Relation Analysis
- Lecture 44 : Auto-Regression Analysis
- Lecture 45 : Logistic Regression (Part-I)
- Lecture 46 : Logistic Regression (Part-II)
- Lecture 47 : Logistic Regression (Part-III)
- Lecture 48 :Tutorial on Logistic Regression
- Lecture 49 : Introduction
- Lecture 50 : Bayes Classification (Part-I)
- Lecture 51 : Bayesian Classification (Part—II)
- Lecture 52 : k-Nearest Neighbor Classification
- Lecture 53 : Tutorial on Classification Techniques
- Lecture 54 : Support Vector Machine (Part-I)
- Lecture 55 : Support Vector Machine (Part-II)
- Lecture 56 : SVM (Part-III)
- Lecture 57 : SVM (Part-IV)
- Lecture 58 : SVM (Part-V)
- Lecture 59 : SVM (Part-VI)
- Lecture 60 : Tutorial on SVM