Modern Algebra
Explore fundamental concepts and advanced topics in modern algebra through a comprehensive series of lectures by Prof. Manindra Agrawal, covering key principles and applications.
Probability and Statistics
Comprehensive exploration of probability and statistics concepts, covering fundamental principles, algebraic structures, and advanced topics through 79 detailed lectures.
Linear Algebra
Comprehensive exploration of linear algebra concepts, from basic operations to advanced topics like eigenvalues, with practical applications and problem-solving techniques.
Linear System Theory
We will cover time invariant, time varying, continuous and discrete time systems. This course will cover concepts of stability, controllability, observability, and design and serve as a necessary foundation for further study in the area of systems and co…
An Introduction to Smooth Manifolds
The course will start with a brief outline of the prerequisites from topology and multi-variable calculus. After that a large class of examples, including Lie groups, will be presented. The course will culminate with proof of Stokes' theorem on manifolds.
Differential Calculus in Several Variables
This is part of a standard course contents in Several Variable Calculus. The idea of the course is to provide students with different backgrounds a common platform to take up further topics in Mathematics, Physics and Engineering.
Linear Algebra
The main purpose of this course is the study of linear operators on finite dimensional vector spaces. The idea is to emphasize the simple geometric notions common to many parts of mathematics and its applications.
Linear Algebra
Comprehensive exploration of linear algebra fundamentals, covering matrices, linear systems, vector spaces, transformations, and inner product spaces, with a focus on theoretical concepts and practical applications.
Algebra 1
Foundational PG level course in Algebra, suitable for M.Sc and first-year Ph.D. students in Mathematics. Learn Permutations. Group Axioms. Order and Conjugacy. Subgroups. Problem solving. Group Actions. Cosets. Group Homomorphisms. Normal subgroups. Quti…
Prime Time - James Maynard
Explore the fascinating world of prime numbers and their impact on our lives with leading researcher James Maynard in this engaging Oxford Mathematics Public Lecture.
Applied Linear Algebra
Explore vector spaces, linear maps, eigenvalues, and applications in systems, algorithms, and optimization. Gain practical skills in linear algebra for various fields.
Introduction to Algebraic Geometry and Commutative Algebra
Explore algebraic geometry and commutative algebra, covering topics from affine algebraic sets to integral extensions. Gain insights into key theorems and principles in this advanced mathematics course.
Commutative Algebra
Comprehensive exploration of commutative algebra concepts through 60 lectures, covering fundamental principles and advanced topics for a deep understanding of algebraic structures.
Computational Commutative Algebra
This is an introductory course in computational commutative algebra. Topics in a typical first course in commutative algebra are developed along with computations in Macaulay2. The emphasis will be on concrete computations, more than on giving complete p…
Face Recognition Techniques and Applications - Lecture 14
Explore face recognition techniques, from simple approaches to advanced methods like PCA and Linear Discriminant Analysis. Learn about eigenvectors, image compression, and scatter matrices.
Matlab Tutorial
Learn MATLAB basics for computer vision: variables, vectors, matrices, image operations, filters, and more. Practical tutorial for beginners and intermediate users.
Linear Algebra Basics for Computer Vision - Lecture 2
Explore essential linear algebra concepts including vectors, matrices, transformations, and SVD, with practical applications in computer vision and machine learning.
Subspace Capsule Network
Explore Subspace Capsule Networks: a novel approach to neural networks that preserves information, norms, and angles. Learn about its principles, applications, and advantages in image classification and generation.
Fundamental Matrix and Stereo Vision - Lecture 13
Explore fundamental matrices, stereo photosynthesis, and random sampling consensus in image processing and computer vision applications.
Facial Recognition: Eigenvectors and Covariance Matrices - Lecture 14
Explore facial recognition technology, from simple approaches to advanced techniques using eigenvectors and covariance matrices. Learn about challenges and solutions in this field.
Basics of Linear Algebra for Computer Vision - Part II - Lecture 3
Explore essential linear algebra concepts for computer vision, including matrix operations, determinants, and transformations, enhancing your understanding of image processing fundamentals.