aut aut      aut aut
   
Maryam Amir Mazlaghani
  Courses 

 Special Topics(SPRING_2018)

Aims:

To make the students fundamentally acquainted with stochastic processes and their applications in Computer Engineering.

Syllabus:

  • مروري بر تئوري احتمال
  • Review of Random Variables
  • Sequence of Random Variables
  • Estimation Theory
  • Detection Theory
  • Stochastic Processes
  • Stationarity
  • Stochastic Linear Systems
  • Power Spectral Density
  • Ergodicity
  • Markov Processes
  • Markov Chains
  • Prediction and Filtering
  • Hidden Markov Models
  • Variational Inference

Text Book:

  • A. Papoulis and S. Pillai, Probability, Random Variables and Stochastic Processes, 4th Edition, McGraw Hill, 2002.


 Special Topics (Applied Linear Algebra)(SPRING_2018)

Aims:

The course consists of the topics of linear algebra and its applications and optimization that are extensively used in different fields of computer engineering. The course contains topics of linear equations, matrices, eigenvalues and eigenvectors, vector spaces, introduction to optimization and linear programming. Also, in simulation exercises, the students will familiarize with the relevant software and applications in computer engineering.

Syllabus:

  • Linear Equations in Linear Algebra (linear systems and their solutions, matrices, the matrix equation, linear independence, linear transformations)
  • Matrix Algebra ( matrix operations, inverse of matrix, matrix factorization, determinants)
  • Vector Spaces ( vector spaces and subspaces, null space, column space, bases, dimension of a vector space, rank, change of basis)
  • Eigenvalues and Eigenvectors (eigenvalues and eigenvectors, characteristic equation, diagonalization, applications)
  • Orthogonality and Least Squares (inner products, orthogonal sets, The Gram-Schmidt process, least squares problems, applications)
  • Singular Value Decomposition, Principal Component Analysis
  • Optimization (vector functions, first and second order derivative, introduction to different types of optimization problems, linear programming, the simplex algorithm)

Text Book:

  • David C. Lay, Steven R. Lay, and Judi J. McDonald, Linear Algebra and its applications, 5th Edition, Pearson, 2015.
  • Philip N. Klein, Coding the Matrix: Linear Algebra through Applications to Computer Science, 1st Edition, Newtonian Press, 2013.


 ٍEngineering Mathematics(FALL_2017)

Aims:

studying Fourier Series, Fourier Transform, Partial Differential Equations, complex analysis, complex integration

Syllabus:

  • orthogonal functions, Fourier series
  • Fourier Integral
  • Fourier Transform and characteristics
  • Partial Differential Equations (PDE)
  • Heat Equation, Wave Equation
  • Solving PDE by Separating Variables
  • solving PDE by Using Fourier Series and Laplace Transform
  • solving PDEs in two dimensions
  • complex numbers
  • complex functions
  • conformal mapping
  • Line Integral in the Complex Plane
  • Cauchy’s Integral Theorem
  • power series
  • Residue Integration Method
  • computing real integral using complex integration

Text Book:

  • E kreyszig , Advanced Engineering Mathematics, 10 th ed. Wiley, 2011.
  • عبدالله شيدفر، رياضيات مهندسي، چاپ شانزدهم، انتشارات دالفك، 1390.


 ٍEngineering Mathematics(FALL_2017)

Aims:

studying Fourier Series, Fourier Transform, Partial Differential Equations, complex analysis, complex integration

Syllabus:

  • orthogonal functions, Fourier series
  • Fourier Integral
  • Fourier Transform and characteristics
  • Partial Differential Equations (PDE)
  • Heat Equation, Wave Equation
  • Solving PDE by Separating Variables
  • solving PDE by Using Fourier Series and Laplace Transform
  • solving PDEs in two dimensions
  • complex numbers
  • complex functions
  • conformal mapping
  • Line Integral in the Complex Plane
  • Cauchy’s Integral Theorem
  • power series
  • Residue Integration Method
  • computing real integral using complex integration

Text Book:

  • E kreyszig , Advanced Engineering Mathematics, 10 th ed. Wiley, 2011.
  • عبدالله شيدفر، رياضيات مهندسي، چاپ شانزدهم، انتشارات دالفك، 1390.


 Optimization(FALL_2017)

Aims:

studying optimization algorithms and their conditions- studying optimization applications

Syllabus:

  • introduction
  • mathematical background
  • convex sets
  • convex functions
  • convex optimization problems
  • duality and optimality conditions
  • optimization application in approximation
  • optimization application in statistical estimation
  • optimization applications in geometric problems
  • unconstrained optimization algorithms
  • equality constrained optimization algorithms
  • constrained optimization algorithms

Text Book:

  • S. Boyed, L. Vandenberg, Convex optimization, Cambridg, 2004
  • J. Nocedal, S. J. Wright, Numerical Optimization, Springer, 1999
  • D. G. Luenberger, Y. Ye, Linear and Nonlinear Programming, Springer, Thired Edition 2008.


 Electronic Circuits(SPRING_2017)

Aims:

analysis and design of electronical circuits

Syllabus:

  • Introduction to electronics-Physics
  • Pn Junction Diode
  • Diode Circuits
  • Bipolar Transistors
  • Bjt Amplifiers
  • Field Effect Transistors
  • MOSFET(Metal oxide Semiconductor FET) Amplifiers

Text Book:

  • Fundamentals of Microelectronics
  • microelectronics circuits


 ٍEngineering Mathematics(SPRING_2017)

Aims:

studying Fourier Series, Fourier Transform, Partial Differential Equations, complex analysis, complex integration

Syllabus:

  • orthogonal functions, Fourier series
  • Fourier Integral
  • Fourier Transform and characteristics
  • Partial Differential Equations (PDE)
  • Heat Equation, Wave Equation
  • Solving PDE by Separating Variables
  • solving PDE by Using Fourier Series and Laplace Transform
  • solving PDEs in two dimensions
  • complex numbers
  • complex functions
  • conformal mapping
  • Line Integral in the Complex Plane
  • Cauchy’s Integral Theorem
  • power series
  • Residue Integration Method
  • computing real integral using complex integration

Text Book:

  • E kreyszig , Advanced Engineering Mathematics, 10 th ed. Wiley, 2011.
  • عبدالله شيدفر، رياضيات مهندسي، چاپ شانزدهم، انتشارات دالفك، 1390.


 Special Topics(SPRING_2017)

Aims:

To make the students fundamentally acquainted with stochastic processes and their applications in Computer Engineering.

Syllabus:

  • مروري بر تئوري احتمال
  • Review of Random Variables
  • Sequence of Random Variables
  • Estimation Theory
  • Detection Theory
  • Stochastic Processes
  • Stationarity
  • Stochastic Linear Systems
  • Power Spectral Density
  • Ergodicity
  • Markov Processes
  • Markov Chains
  • Prediction and Filtering
  • Hidden Markov Models
  • Monte Carlo Methods

Text Book:

  • A. Papoulis and S. Pillai, Probability, Random Variables and Stochastic Processes, 4th Edition, McGraw Hill, 2002.


 ٍEngineering Mathematics(FALL_2016)

Aims:

studying Fourier Series, Fourier Transform, Partial Differential Equations, complex analysis, complex integration

Syllabus:

  • orthogonal functions, Fourier series
  • Fourier Integral
  • Fourier Transform and characteristics
  • Partial Differential Equations (PDE)
  • Heat Equation, Wave Equation
  • Solving PDE by Separating Variables
  • solving PDE by Using Fourier Series and Laplace Transform
  • solving PDEs in two dimensions
  • complex numbers
  • complex functions
  • conformal mapping
  • Line Integral in the Complex Plane
  • Cauchy’s Integral Theorem
  • power series
  • Residue Integration Method
  • computing real integral using complex integration

Text Book:

  • E kreyszig , Advanced Engineering Mathematics, 10 th ed. Wiley, 2011.
  • عبدالله شيدفر، رياضيات مهندسي، چاپ شانزدهم، انتشارات دالفك، 1390.


 Optimization(FALL_2016)

Aims:

studying optimization algorithms and their conditions- studying optimization applications

Syllabus:

  • introduction
  • mathematical background
  • convex sets
  • convex functions
  • convex optimization problems
  • duality and optimality conditions
  • optimization application in approximation
  • optimization application in statistical estimation
  • optimization applications in geometric problems
  • unconstrained optimization algorithms
  • equality constrained optimization algorithms
  • constrained optimization algorithms

Text Book:

  • S. Boyed, L. Vandenberg, Convex optimization, Cambridg, 2004
  • J. Nocedal, S. J. Wright, Numerical Optimization, Springer, 1999
  • D. G. Luenberger, Y. Ye, Linear and Nonlinear Programming, Springer, Thired Edition 2008.


 
 
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