
Explore the basics of sets, vectors, and matrices in mathematics. See how data forms like marks and tables become vectors and matrices, and distinguish vectors from scalars.
Explore vectors as quantities with magnitude and direction, illustrated by displacement and velocity, and represent them as directed line segments using unit vectors along the X, Y, and Z axes.
Explore algebraic operations on vectors, including scalar multiplication, vector addition and subtraction, dot and cross products, and the geometric interpretations with parallelograms and unit vectors.
Explore scalar and vector triple products, their dot and cross definitions, determinant and parallelepiped interpretations, and Lagrange's expansion for simplifying vector calculations.
Identify linear dependence by finding nonzero scalars alpha_i with sum alpha_i V_i = 0. Use determinant of the coefficient matrix to distinguish independence, and study V1, V2, V3, V4 examples.
Explore orthogonal vectors and orthonormal vectors, where the dot product is zero and unit vectors have length one, and a real matrix is orthogonal when its inverse equals its transpose.
Normalize a vector to unit length to preserve direction and use unit vectors to indicate direction. Compute vector and scalar projections of A onto B to get components and work.
Explore how matrices organize data as rectangular arrays of numbers in rows and columns, and learn key operations like determinant, transpose, and inverse, including identity matrices and cofactors.
Explore real matrices and their types—symmetric, skew symmetric, and orthogonal—through transposes, the identity, and determinant conditions, with examples identifying each type.
Explore complex matrices and their complex conjugates, use transposed conjugates to identify hermitian and skew hermitian forms and determine unitary matrices that yield the identity.
solving systems of linear equations covers a x = b, giving x = b/a for a ≠ 0, and infinite or no solutions when a = 0; includes graphical, substitution, elimination, and augmented-matrix methods.
Explore how equivalent linear systems share same solutions and simplify them with elementary row operations, interchanging rows, scaling by a non-zero constant, and adding multiples of rows via augmented matrices.
Explore Gaussian elimination, transforming the augmented matrix to upper triangular form via forward elimination with elementary row operations, using pivots to reveal rank, determinant, and inverse.
Describe row echelon form with zero bottom rows and pivots moving right; obtain reduced row echelon form via Gorst-Jordan method, and identify basic versus non-pivot variables in augmented form.
Determine the rank of a matrix by reducing to row echelon form, identify pivots, and relate rank to the solution counts of linear systems using augmented and coefficient matrices.
Transform a matrix to canonical form using elementary operations, then count nonzero rows to obtain its rank; the example yields a rank of three.
Explore invertible, square matrices with full rank and nonzero determinant, and their unique inverses. Learn adjugate-based computation and elementary matrices, plus left and right inverses for non-square cases.
Explore theorems on the inverse of matrices, linking invertibility to full rank, the identity matrix, and products of elementary matrices, with BA = I and AB = I.
Apply gauss-jordan elimination with elementary row operations to the augmented matrix, transforming it to reduced row echelon form to obtain the inverse on the right-hand side.
Explore homogeneous and non homogeneous linear systems, their matrix forms and determinants. Identify when solutions are unique, infinite, or no solutions by examining rank and the coefficient matrix.
Explore eigenvalues and eigenvectors to diagonalize matrices, power computations, and fast vibrational analysis, with real-world applications in resonance, natural frequencies, and diverse scientific fields.
The Cayley-Hamilton theorem states that every square matrix satisfies its own characteristic polynomial. It shows the matrix annihilates its own characteristic equation, enabling finding inverses and higher powers.
Apply the Gram-Schmidt method to form an orthogonal (and optionally normalized) set that spans subspace, using projections and noting Householder transformation, Given's rotation, and singular value decomposition for numerical stability.
Explore diagonalization of a matrix by using eigenvalues and eigenvectors to form a diagonal matrix via A = P D P inverse, and solve transformed systems for simplified, canonical forms.
Explore matrix decomposition, the factorization of a matrix into upper and lower triangular matrices, such as the LU decomposition, and its use in solving linear systems, inverses, and determinants.
Define sequences as ordered lists and as mappings from natural numbers to real numbers, exploring domain, range, codomain, monotone behavior, and bounded above or below.
In engineering mathematics, explore convergence and divergence of sequences, define the limit of a sequence, and distinguish convergent, divergent, and oscillatory behavior with epsilon criteria and real limit values.
Explore infinite series in engineering mathematics, including convergence and partial sums, and learn how any periodic function can be expressed as a sine–cosine series using summation notation.
Classify series as positive-term, alternating, and geometric, with convergence criteria for geometric sums and 0≤R<1. Address harmonic and B-series, noting 1/n^2 converges while harmonic diverges.
Apply the limit form of the comparison test to decide convergence or divergence of positive-term series by comparing with a known convergent geometric series (and 1/n^2), using the limit l.
Explore d'Alembert's ratio test for series with positive terms, using the limit of a_{n+1}/a_n to determine convergence or divergence, illustrated by factorial-based examples.
Raabe’s test analyzes a series to decide convergence; with ratio test limit one, convergence occurs for k less than or equal to zero and divergence for k greater than zero.
Apply Cauchy’s root test to positive-term series by the limit of the nth root; conclude convergence if the limit is less than one, divergent if greater than one.
Apply the logarithmic test to determine convergence of positive-term series, with outcomes for L>1, L<1, and inconclusive L=1. Compare with the ratio test via an example with factorials and powers.
Explains alternating series and the Leibniz test, showing how two conditions on alternating signs determine convergence and illustrating convergent and divergent examples.
Explore real intervals, including open, closed, and half-open forms with end points, and master continuity, differentiability, and derivatives for polynomials, trigonometric, and exponential functions in engineering mathematics.
Explore how the mean value theorem links change to the derivative, ensuring an instantaneous velocity equals the average velocity, and illustrate Rolle's theorem with a tangent parallel to the x-axis.
Explain lagrange’s mean value theorem: a continuous function on a closed interval and differentiable on the open interval has a point c with f′(c) equal to the average rate.
Explore Cauchy's mean value theorem, also called the extended or second mean value theorem, linking derivatives to changes in two functions on a closed interval.
Explore generalized mean value theorems and Taylor series, showing how polynomial and infinite series approximate functions, with Maclaurin and Lagrange remainder forms, and applications to differential equations and optimization.
Explains Taylor's theorem and its conditions on intervals, then derives Maclaurin series expansions, including the log(1+x) expansion: x - x^2/2 + x^3/3 - x^4/4.
Explore applications of definite integrals in Cartesian coordinates to compute areas under curves, arc lengths, surface areas of revolution, and volumes generated by revolving regions about the x-axis or y-axis.
In this course you will learn about Engineering Mathematics in a playful way. Each and every topic is prepared in such a way that explains conceptually. This engineering mathematics course covers matrices, eigen values and eigen vectors, sequences and series, calculus, partial differentiation and applications. Some of the topics contain infographic information to get a clear view.