B.Sc Mathematics Video Lectures

All these B.Sc Mathematics video lectures is a part of ONLINE LIVE class taken by either Biswadip Sir or Sudipta Ma’am with B.Sc Maths Honours/Pass students, who are studying under University of Calcutta.
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Mrittika Nandi | B.Sc Math Honours | Semester Topper | Basanti Devi College | Calcutta University

YouTube Video VVUwWGhXU3VXcWcxMEE3SEs2dmVxMUV3Lk0wNGFSaGNWcFlV

Shreya Datta | B.Sc Math Honours | Semester Topper | Vivekananda College | Calcutta University

YouTube Video VVUwWGhXU3VXcWcxMEE3SEs2dmVxMUV3LnJDNnFmd1doT1RZ

Fermat's theorem - application of Fermat's little theorem | least positive residue finding example

YouTube Video VVUwWGhXU3VXcWcxMEE3SEs2dmVxMUV3LjRvbWVzZGpRNFdr

Solution of Linear Congruence - congruences | Solve the linear congruence 12x ≡ 9(mod 15) | CU Maths

YouTube Video VVUwWGhXU3VXcWcxMEE3SEs2dmVxMUV3LmFUbDMyVy1hMG1n

Equivalence relation - definition, example, solved problems & theorems (proof) | Abstract algebra L2

YouTube Video VVUwWGhXU3VXcWcxMEE3SEs2dmVxMUV3LjQ0bzBzS0tBeEQ0

L'Hôpital's rule (or L'Hospital's rule) and Indeterminate Forms - Calculus | CC1 | Sem 1 | CU | VU

YouTube Video VVUwWGhXU3VXcWcxMEE3SEs2dmVxMUV3LmUxRDZxb2xjUTY0

Relation (or binary relation) - definition, concept & examples | Types of Relation | CC2 | Sem 1

YouTube Video VVUwWGhXU3VXcWcxMEE3SEs2dmVxMUV3Lk14ZlZsdDZrOVFB

Solution of system of linear equations using matrices row echelon form | Consistency & inconsistency

YouTube Video VVUwWGhXU3VXcWcxMEE3SEs2dmVxMUV3Lm84a2E3clF3ZVdJ

Complex numbers -Solved problem | Example on complex numbers

YouTube Video VVUwWGhXU3VXcWcxMEE3SEs2dmVxMUV3LmZpZnNzdFhqNVhB

Linear transformation of vector space | Linear transformation & matrices | Linear mapping - examples

YouTube Video VVUwWGhXU3VXcWcxMEE3SEs2dmVxMUV3LlQwWU9hVHBLN2hr

$Vectors Space - Linear Independence, spanning subsets | Basis & dimension of subspaces of Rn | Lec 2 Vector space - Rn over R as a vector space and its subspaces | Lecture -1 | Linear Algebra Following vector space syllabus will be coved: Day 1. R^n over R as a vector space and its subspaces. (Link https://youtu.be/P5kghnkdpgU ) Day 2. Linear Independence and Spanning Subsets in R^n . Basis & Dimension of subspaces of R^n. Day 3. Introduction to linear transformations as structure preserving maps, matrix of a linear transformation. Day 4. Matrix equation Ax=b, Solution space of a system of homogenous linear equations is a subspace. Day 5. Eigen values, eigen vectors and characteristic equation of a matrix. Cayley-Hamilton theorem and its use in finding the inverse of an invertible matrix. ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ C PROGRAMMING LANGUAGE book https://amzn.to/3iIXU45 CBCS BSc Math Honours syllabus + best books https://youtu.be/2UCMJftJ_Wc Best CBCS Books for B.Sc Maths link: https://cumaths.com/cbcs-bsc-mathemat... B.Sc Maths Books written by Biswadip Sir link: https://amzn.to/2GDgTzs ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ We provide ONLINE coaching classes for B.Sc Mathematics (all semester) under CBCS, both Honours & Pass course (Core & Generic) for students of Calcutta University(CU). New Batches start from every semester of CU. FIND YOUR NEAREST STUDY CENTER HERE: https://cumaths.com/ B.Sc Mathematics Online classes, both Hons. & pass, are taken by Prof. Sudipta Ma’am and Prof. Biswadip Sir, Ramakrishna Mission Residential College (Autonomous), Narendrapur. Join our online B.Sc Maths classes to achieve best result in B.Sc Mathematics. ✓ B.Sc Mathematics (Hons & Pass) Online Classes ✓ B.Sc Mathematics Online Coaching for 1st year, 2nd year & 3rd year students ✓ Tuition for B.Sc Maths - Semester 1 to Semester 6 ✓ Regular LIVE class & pre-recorded video lectures for B.Sc Mathematics ✓ Mock tests & special doubt-clearing classes ✆ Call/WhatsApp 9088261414/ 9007293439 For more info visit https://cumaths.com/ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ Like our fb page https://www.facebook.com/bscmathematics For support us: https://rzp.io/l/CavRtYe PayPal https://www.paypal.me/cumaths Last but not least, subscribe us, like us and comment your suggestions. Study hard, learn everything that you want. Good luck. #BPSir #CUMATHS #BiswadipSir ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ ̴ Linear Combinations and Span Let v1, v2,…, vr be vectors in Rn . A linear combination of these vectors is any expression of the form k1v1+k2v2+...+krvr where the coefficients k1, k2,…, kr are scalars. The set of all linear combinations of a collection of vectors v1, v2,…, vr from Rn is called the span of { v1, v2,…, vr }. Linear Independence Let A = { v 1, v 2, …, v r } be a collection of vectors from Rn . If r is greater than 2 and at least one of the vectors in A can be written as a linear combination of the others, then A is said to be linearly dependent. The Rank of a Matrix The maximum number of linearly independent rows in a matrix A is called the row rank of A, and the maximum number of linarly independent columns in A is called the column rank of A. For any matrix A, the row rank of A = the column rank of A Because of this fact, there is no reason to distinguish between row rank and column rank; the common value is simply called the rank of the matrix. A Basis for a Vector Space Let V be a subspace of Rn for some n. A collection B = { v 1, v 2, …, v r } of vectors from V is said to be a basis for V if B is linearly independent and spans V. If either one of these criterial is not satisfied, then the collection is not a basis for V.$

Vectors Space - Linear Independence, spanning subsets | Basis & dimension of subspaces of Rn | Lec 2

YouTube Video VVUwWGhXU3VXcWcxMEE3SEs2dmVxMUV3LjZZN0IyWTJNdW9Z

Vector space - Rn over R as a vector space and its subspaces | Lecture -1 | Linear Algebra

YouTube Video VVUwWGhXU3VXcWcxMEE3SEs2dmVxMUV3LlA1a2dobmtkcGdV

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