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Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Important dates
Course Commencement Date
Start Date : 20 Jan, 2025
End Date : 11 Apr, 2025
Enrollment Date
End Date : 27 Jan, 2025
Certificate Exam Date
Start Date : 03 May, 2025
Other
End Date : 14 Feb, 2025
Courses and Certificate Fees
| Fees Informations | Certificate Availability | Certificate Providing Authority |
|---|---|---|
| INR 1000 | yes | IIT Kharagpur |
The Syllabus
- Vectors, operations on vectors, vector spaces and subspaces,inner product and vector norm, linear dependence and independence, Matrices, linear transformations, orthogonal matrices
- System of linear equations, existence and uniqueness, left and right inverses, pseudo inverse, triangular systems
- LU decomposition and computational complexity, rotators and reflectors, QR decomposition, Gram Schmidt Orthogonalization
- Condition number of a square matrix, geometric interpretation, norm of matrix, sensitivity analysis results for the system of linear equations
- Linear least squares, existence and uniqueness, geometrical interpretation, data fitting with least squares, feature engineering, application to Vector auto-regressive models, fitting with continuous and discontinuous piecewise linear functions
- Application of least squares to classification, two-class and multi-class least squares classifiers, Polynomial classifiers, application to MNIST data set
- Multi-objective least squares, applications to estimation and regularized inversion, regularized data fitting and application to image de-blurring, constrained least squares, application to portfolio optimization
- Eigenvalue eigenvector decomposition of square matrices,spectral theorem for symmetric matrices
- SVD, relation to condition number, sensitivity analysis of least squares problems, variation in parameter estimates in regression
- Multicollinearity problem and applications to principal component analysis (PCA) and diinensionality reduction, power method, application to Google page ranking algorithm
- Underdetermined systems of linear equations, least norm solutions, sparse solutions, applications in dictionary learning and sparse code recovery, inverse eigenvalue problem, application in construction of Markov chains from the given stationary distribution
- Low rank approximation (LRA) and structured low rank approximation problem (SLRA), application to model order selection in time series, alternating projections for computing LRA and SLRA
Instructors
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2 Jan'25, 07:13 AM
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31 Dec'24, 07:24 AM
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31 Dec'24, 05:52 AM
31 Dec'24, 05:40 AM
30 Dec'24, 12:55 PM
30 Dec'24, 10:47 AM
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