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Week - 1 |
Python an overview: vectors, arrays, data types, loops, functions, logical operations, functions and other features, Introduction to numerical methods: Solution of equations of a single variable |
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Week - 2 |
Curve fitting (approximation), interpolation and regression methods: Least Squares regression, Linear Regression, Linearization of nonlinear data, polynomial regression, spline interpolation, Fourier approximation and interpolation |
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Week - 3 |
Finite difference approximation of the derivative: Finite difference formulas for the derivative, Finite difference formulas using Taylor Series expansion, Differentiation using Lagrange Polynomials, Differentiation using curve fitting, Richardson extrapolation, Error in numerical differentiation, Summary of numerical differentiation |
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Week - 4 |
Numerical integration: Rectangle, midpoint, and trapezoidal methods (Newton-Cotes formulars), Simpson’s method, Gauss quadrature, Richardson extrapolation, Romberg integration |
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Week - 5 |
Midterm 1 |
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Week - 6 |
Ordinary differential equations – Initial value problems: Euler’s methods, Modified Euler’s method, Midpoint Method, Runge-Kutta Methods, Multi step methods |
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Week - 7 |
Ordinary differential equations – Boundary value problems: The shooting method, Finite difference method |
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Week - 8 |
Introduction to linear programming: Simplex method |
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Week - 9 |
Introduction to unconstrained nonlinear programming: Random search methods, Gradient descent method, Steepest descent method, Newton’s method |
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Week - 10 |
Introduction to constrained nonlinear programming: A basic approach to penalty function methods |
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Week - 11 |
Midterm 2 |
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Week - 12 |
Modern optimization methods: Genetic algorithms |
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Week - 13 |
Modern optimization methods: Simulated annealing |
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Week - 14 |
Modern optimization methods: Particle swarm optimization |
Eskisehir Technical University Info Package 
Turkish 
English