Course Code  EE 313 
Course Title  Applied Mathematics for Engineers and Scientists 
Credit Hours  03 
Prerequisites by Course(s) and Topics  Junior Standing 
Assessment Instruments  Homework Quizzes Assignments   Midterm Final 

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Current Catalog Description  The objective of the course is three fold: (a) to introduce the student to the basics of probability theory, using axiomatic approach, and familiarize the student with computation of moments of random variables, (b) to make the student comfortable with formulating engineering problems in terms of vectors and matrices and then using matrix and vector space properties to solve the problems and (c) to provide the student with a solid mathematical foundation in differentiation and integration. 
Textbook (or Laboratory Manual for Laboratory Courses)  Probability, Random Variables and Stochastic Processes by Athanasios Papoulis and S. U. Pillai, 4^{th} Edition, McGraw Hill. Linear Algebra and Its Applications by G. Strang, Harcourt Brace Jovanovich. 
Reference Material  Probability and Random Processes for Electrical Engineering by Alberto Leon Garcia, 2^{nd} Edition, Prentice Hall. Calculus and Analytic Geometry by G. Thomas and R. Finney, AddisonWesley. 
Course Goals  Upon completion of this course, students will: Know how to use various computational tools of differentiation and integration in solving engineering problems. Understand random variables, moments and expectations. Understand vector space concepts and become familiar with geometric concepts such as orthogonality, projections, eigenvalues and eigenvectors. 
Topics Covered in the Course  Basic Concepts [Axiomatic Probability Theory, Discrete Probability Space and Independent Events] Repeated Trials [Single and Multiple Events, Distributions – Binomial and Gaussian] Random Variables [Distribution and Density Functions, Conditional density functions] Functions of A Random Variable, Y = g(X) [Distribution and Density Functions, Conditional density functions] Functions of Two Random Variables, Z = g(X, Y) [Distribution and Density Functions, Conditional density functions] Moment Generating Functions [First and Second order Moments, Conditional Moments and Characteristic Functions] Vector Spaces and Linear Equations [Subspaces, Linear Independence, Basis and Dimension] Orthogonality [Inner products, Projections, LeastSquare Approximations and GramSchmidt Orthogonalization] Determinants [Properties, Formulae and Applications] Sequence of Random Variables [Sum, Product and Random Vector] Eigenvalues and Eigenvectors [Applications and Study of Hermitian Matrices] 
Laboratory Projects/Experiments Done in the Course  N/A 
Programming Assignments Done in the Course  N/A 
Class Time Spent on (in credit hours)  Theory  Problem Analysis  Solution Design  Social and Ethical Issues 
80%  20%     
Oral and Written Communications  N/A 