Learn · Statistical Foundation for Big Data Analysis
Lecture 21 of 60Week 5
Lecture 21 : Bias Variance Decomposition
Prof. Arindam Banerjee · IIT Kharagpur
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- Statistical Foundation for Big Data Analysis, IIT Kharagpur (at NPTEL)
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All the lectures
- Lecture 1 : Big Data : Definition, Examples and Challenges
- Lecture 2 : Big Data Analysis vs Big Data Processing : Role of Dimensional Statistics.
- Lecture 3 : Statistical Framework 1
- Lecture 4 : Statistical Framework 2
- Lecture 5 : Statistical Framework 3
- Lecture 06 : Point Estimation: Unbiased Estimation
- Lecture 07 : Point Estimation: Likelihood 1
- Lecture 08 : Point Estimation: Likelihood 2
- Lecture 09 : Method of Moments
- Lecture 10 : Point Estimation: Asymptotics
- Lecture 11 :Interval Estimation 1
- Lecture 12 : Interval Estimation 2
- Lecture 13 : Basics of Hypothesis Testing-I
- Lecture 14 : Basics of Hypothesis Testing-II
- Lecture 15 : Basics of Hypothesis Testing-lll
- Lecture 16 :Bias Variance and Mean Squared Errors
- Lecture 17 :Bias Variance and Mean Squared Errors
- Lecture 18 :Bias Variance and Mean Squared Errors
- Lecture 19 :Bias Variance and Mean Squared Errors
- Lecture 20 : Bias Variance and Mean Squared Errors
- Lecture 21 : Bias Variance Decomposition
- Lecture 22 : Cross Validation and Bias Variance Tradeoff
- Lecture 23 : Methods of Cross Validation
- Lecture 24 : Regression vs Classification
- Lecture 25 : Inference on Logistic Regression
- Lecture 26 : Regression: Linear Regression
- Lecture 27 : Regression: Linear Regression 2
- Lecture 28 : Regression: Multiple Linear Regression 1
- Lecture 29 : Regression: Multiple Linear Regression 2
- Lecture 30 : Regression: Multiple Linear Regression 3
- Lecture 31 : Generalities of Multivariate Distribution
- Lecture 32 : Bivariate Normal Distribution
- Lecture 33 : Multivariate Normal Distribution 1
- Lecture 34 : Multivariate Normal Distribution 2
- Lecture 35 : Multivariate Normal Distribution 3
- Lecture 36 : Supervised Learning vs Unsupervised Learning
- Lecture 37 : Heirarchical Clustering
- Lecture 38 : K-means clustering
- Lecture 39 : Curse of Dimensionality
- Lecture 40 : Behaviour of Volume in High Dimension
- Lecture 41 : Introduction to Principal Component Analysis
- Lecture 42 : Eigenvalues and Eigenvectors
- Lecture 43 : Mathematical Formulation of Principal Component Analysis
- Lecture 44 : Mathematical Foundation of Principal Component Analysis
- Lecture 45 : Population Principal Component Analysis for Normal Distribution
- Lecture 46 : Population vs Sample Principal Component Analysis
- Lecture 47 : Mathematical Formulation of Sample PCA
- Lecture 48 : Sample PCA for Covariance matrix and Correlation Matrix
- Lecture 49 : Examples of Sample PCA
- Lecture 50 : Inference on Principal Component Analysis
- Lecture 51 : Network Data and Random Graph
- Lecture 52 : Properties of Random Graph
- Lecture 53 : Law of Large Numbers for Random Graph 1
- Lecture 54 : Law of Large Numbers for Random Graph 2
- Lecture 55 : Law of Large Numbers for Random Graph 3
- Lecture 56 : Introduction to Network Medicine
- Lecture 57 :Selected Case Study on Network Medicine
- Lecture 58 : Social Network Analysis
- Lecture 59 : Simple Linear Regression on Social Network Data
- Lecture 60 : Conclusion and Course Summary