Syllabus, schedule, readings, references, homework assignments, and message board. This course is a basic course offered to UG/PG students of Engineering/Science background. Currently, he is supervising eight research students.Linear dependence and independence, Bases and Dimensions – I, Bases and Dimension - II, Linear Transformation - I, Orthogonal subspaces, Row space, column space and null Space, Eigenvalues and Eigenvectors-I, Eigenvalues and Orthogonal Sets, Gram Schmidt orthogonalization and orthonormal bases, Introduction to Matlab, Sign integer representationFloating point representation, Round-off error, Error propagation in computer arithmetic, Addition and multiplication of floating Conditioning and condition numbers-II, Stability of numerical algorithms-I, Stability of numerical algorithms-II, Vector Estimation of the condition number, Singular value decomposition of a matrix – I, Singular value decomposition of a : SVD and their applications, Perturbation theorem for singular values, Outer product expansion of a matrix, Least : Psudeo - inverse and least square solution, Householder matrices and their applications, Householder QR factorization –I, Dr. D. N. Pandey is an Associate Professor in the Department of Mathematics, IIT Roorkee. ), but when I’m trying to solve a practical problem, code is more useful than theory. Also, one of the tests of whether you truly understand something is if you can code it, so this course if much more code-centered than a typical numerical linear algebra course.You can ask questions or share your thoughts and resources using the There is no official registration for the course; rather it is up to you to work through the materials at your own pace. In addition to classical applications, most examples will particularly focus on modern large-scale machine learning problems. This course builds on elementary linear algebra and in it we derive, describe and analyse a number of widely used constructive methods (algorithms) for various problems involving matrices. He delivered 13 video lectures on Engineering Mathematics in NPTEL Phase I and recently completed Pedagogy project on Engineering Mathematics jointly with Dr. Uaday Singh in the same Department. 3. Further he has completed online certification course “Mathematical methods and its applications” jointly with Dr. S.K.
Students will be taught a range of analytical and algorithmic tools that are employed in research and industry, such as various matrix types, their properties and factorisations, iterative algorithms for matrix computations such as Krylov subspace methods, various eigen-solvers, elements of convex and non-convex analysis, derivative free as well as first and second-order optimisation methods, constrained and unconstrained optimisation algorithms, and introduction to non-smooth and stochastic optimisation. The problems with this are that students often lose motivation, don’t have a sense of the “big picture”, and don’t know which pieces they’ll even end up needing. Furthermore, some math problems are not very stable, meaning that if you vary the input a little, you get a vastly different output. It contains basics of matrix algebra, computer arithmetic, conditioning and condition number, stability of numerical algorithms, vector and matrix norms, convergent matrices, stability of non-linear systems, sensitivity analysis, singular value decomposition (SVD), algebraic and geometric properties of SVD, least square solutions, Householder matrices and applications, QR method, Power method and applications, Jacobi method for finding the eigenvalues of a given matrix. By Prof. P. N. Agarwal, Prof. D. N. Pandey | Numerical linear algebra is concerned with the practical implications of implementing and executing matrix operations in computers with real data. His area of expertise includes semigroup theory and functional differential equations of fractional and integral orders. Rounding errors can add up, particularly if you are iterating!
Toggle navigation. Implementations will be done using MATLAB/Python. Currently, he is supervising five research students., Morning session 9am to 12 noon; Afternoon Session 2pm to 5pm.YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. In this course, students will learn about the theory and practical aspects of many fundamental tools from matrix computations, numerical linear algebra and optimisation. This course is a basic course offered to UG/PG students of Engineering/Science background. The company fast.ai released a free course titled “Computational Linear Algebra” […]
We have been inspired by Harvard professor I love math (I even have a math PhD! This course is a basic course offered to UG/PG students of Engineering/Science background. 2. In addition to classical applications, most examples will particularly focus on modern large-scale machine learning problems. In this course, students will learn about the theory and practical aspects of many fundamental tools from matrix computations, numerical linear algebra and optimisation. This course will be offered during period 2-3 every second year. He has already prepared e-notes for the course titled “Ordinary Differential Equations and Special Functions” under e-Pathshala funded by UGC. Computational Linear Algebra for Coders. Audience. 4.