Learn · Data Science for Engineers
Lecture 13 of 50Week 2
Solving Linear Equations
Prof. Ragunathan Rengasamy and Prof. Shankar Narasimhan · IIT Madras
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- Data Science for Engineers, IIT Madras (at NPTEL)
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
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All the lectures
- Data science for engineers Course philosophy and expectation
- Introduction to R
- Introduction to R (Continued)
- Variables and datatypes in R
- Data frames
- Recasting and joining of dataframes
- Arithmetic,Logical and Matrix operations in R
- Advanced programming in R : Functions
- Advanced Programming in R : Functions (Continued)
- Control structures
- Data visualization in R Basic graphics
- Linear Algebra for Data science
- Solving Linear Equations
- Solving Linear Equations ( Continued )
- Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors
- Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 1)
- Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 2 )
- Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 3 )
- Statistical Modelling
- Random Variables and Probability Mass/Density Functions
- Sample Statistics
- Hypotheses Testing
- Optimization for Data Science
- Unconstrained Multivariate Optimization
- Unconstrained Multivariate Optimization ( Continued )
- Gradient ( Steepest ) Descent ( OR ) Learning Rule
- Multivariate Optimization With Equality Constraints
- Multivariate Optimization With Inequality Constraints
- Introduction to Data Science
- Solving Data Analysis Problems - A Guided Thought Process
- Module : Predictive Modelling
- Linear Regression
- Model Assessment
- Diagnostics to Improve Linear Model Fit
- Simple Linear Regression Model Building
- Simple Linear Regression Model Assessment
- Simple Linear Regression Model Assessment ( Continued )
- Muliple Linear Regression
- Cross Validation
- Multiple Linear Regression Modelling Building and Selection
- Classification
- Logisitic Regression
- Logisitic Regression ( Continued )
- Performance Measures
- Logisitic Regression Implementation in R
- K - Nearest Neighbors (kNN)
- K - Nearest Neighbors implementation in R
- K - means Clustering
- K - means implementation in R
- Data Science for engineers - Summary