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University of Mumbai Semester 3 (SE Second Year) Applied Mathematics 3 Revised Syllabus
University of Mumbai Semester 3 (SE Second Year) Applied Mathematics 3 and their Unit wise marks distribution
Units and Topics
| # | Unit/Topic | Marks |
|---|---|---|
| 100 | The Laplace Transform | - |
| 200 | Matrices | - |
| 300 | Complex Analysis | - |
| 400 | Complex Integration | - |
| 500 | Statistics | - |
| 600 | Optimization | - |
| Total | - |
Syllabus
100 The Laplace Transform
- Definition and properties (without proofs)
- All standard transform methods for elementary functions including hyperbolic functions; Heaviside unit step function, Dirac delta function; the error function
- Evaluation of integrals using Laplace transforms
- Inverse Laplace transforms using partial fractions and H(t-a)
- Convolution (no proof).
200 Matrices
- Eigen values and eigen spaces of 2x2 and 3x3 matrices
- Existence of a basis and finding the dimension of the eigen space (no proofs)
- Nondiagonalisable matrices
- Minimal polynomial
- Cayley - Hamilton theorem (no proof)
- Quadratic forms
- Orthogonal and congruent reduction of a quadratic form in 2 or 3 variables; rank, index, signature; definite and indefinite forms.
300 Complex Analysis
- Cauchy-Riemann equations (only in Cartesian coordinates) for an analytic function (no proof)
- Harmonic function
- Laplace’s equation; harmonic conjugates and orthogonal trajectories (Cartesian coordinates); to find f(z) when u+v or u - v are given
- Milne-Thomson method; cross-ratio (no proofs); conformal mappings; images of straight lines and circles.
400 Complex Integration
- Complex Integration Cauchy’s integral formula; poles and residues
- Cauchy’s residue theorem
- Applications to evaluate real integrals of trigonometric functions
- Integrals in the upper half plane; the argument principle.
500 Statistics
- Mean, median, variance, standard deviation
- Binomial, Poisson and normal distributions
- Correlation and regression between 2 variables.
- Note:- No theory questions expected in this module
600 Optimization
- Non-linear programming
- Lagrange multiplier method for 2 or 3 variables with at most 2 constraints
- Conditions on the Hessian matrix (no proof)
- Kuhn-Tucker conditions with at most 2 constraints.
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