Fall Term 2020\2021
Faculty of Computing and Information Technology
Computer Science Department
CPCS-212 Applied Math for Computing Syllabus
Prerequisite: MATH-202
Credit: 4 hours
The objective of this course is to familiarize students with the basic concepts of applied
mathematics used in computer science. Topics include: Matlab: matrices and arrays, Matlab:
graphics, Matlab: programming, solution of nonlinear equations, solution of systems of linear
equations, numerical integration, numerical differentiation, and ordinary differential equations.
Teacher Information
Dr. Dalal Zahran E-mail: dzahran@kau.edu.sa Online hours: Sun, Tues 10-12:30
Dr. Sanaa Sharaf E-mail: ssharaf@kau.edu.sa Online hours: Sun, Tues, Thurs 11-1
Class Schedule
Dr. Dalal Zahran: Section AAR: Sun, Tues, Thurs: 8– 8:50
Section CAR: Sun, Tues, Thurs: 9– 9:50
Dr. Sanaa Sharaf: Section EAR: Sun, Tues, Thurs: 10– 10:50
Lab: I. Hanadi Alfaridi
Lab: I. Hanadi Alfaridi
Lab: I. Noha Alnahdi
Student Assessment
Assessment
Grade
Date
HomeWorks
5
Assigned at lectures
Mid Exam
30
Week 9: Sun 25 October 2020
Project
15
Week 12: Sun 15 November 2020
Lab
20
With lab instructor
Final Exam
30
Assigned by KAU
Textbook
Applied Numerical Analysis, 7th ed. (2004), by Gerald & Wheatley, Addison-Wesley.
Topics Coverage
Rules and Regulations
1. Student is supposed to attend all online classes on time. Absence of 25% of total
online classes without a genuine excuse will result in denied entry to the final exam,
consequently the student will get DN (Denial) grade.
2. Lab attendance is mandatory. If you miss more than 25% of online labs, you will not
be allowed to take the final lab exam.
3. Make-up exam is only for the student who submit a medical sick leave from a
government or trusted hospital within 5 days after the missed exam.
4. Any makeup exam for an excused student will be held at the end of the term as
appointed by the course instructors. It will include all chapters of the curriculum.
5. A zero (0) will be assigned for any cheating attempts on exam, homework, or project
whether it is in the class or lab.
6. Exam Instructions:
1. Mid and Final exam: will be in KAU Campus.
2. Scientific calculator is prohibited (use very basic calculator: (+, -, *, /, ex).
3. Calculators are not allowed to be shared between examinees.
4. Write with a blue pen at the end of the exam.
7. It is your responsibility to check Blackboard regularly for announcements and posted
updates. However, some clarifications may only be given in the class, so make sure you
keep up with all announcements.
8. Use your KAU student e-mail only to communication with your course instructors and
include your course number and section.
9. NO BONUS will be given in this course.
Course Learning Outcomes
By completion of the course the students should be able to:
1. Recognize basic data structures in Matlab. (a)
2. Recognize basic matrix mathematics in Matlab. (a)
3. State techniques for plotting data in Matlab. (i)
4. State programming fundamentals in Matlab (i)
5. *Calculate the roots using the idea of a numerical method (Bisection method, Newton
method, Secant method) to locate roots of an algebraic equation. (a)
6. Apply a numerical method (Bisection method, Newton method, Secant method) to locate
roots of an algebraic equation. (j)
7. Produce a program for a numerical method (Gaussian elimination method, Gaussian
elimination with scaled partial pivoting method) to solve a system of linear equations in
Matlab (i)
8. Apply a numerical method (Gaussian elimination method, Gaussian elimination with
scaled partial pivoting method) to solve a system of linear equations. (j)
9. *Produce a program for a numerical method (Bisection method, Newton method, Secant
method) to roots of an algebraic equation in Matlab. (i)
10. Apply a numerical method (Direct Interpolation And Least Square Regression ) to
interpolate or curve fit discrete points (j)
11. *Apply a numerical method (Upper and lower sums, Newton-Cotes methods) to find the
numerical integration of a function. (j)
12. Produce a program for a numerical method (Upper and lower sums, Newton-Cotes
methods) to find the numerical integration of a function in Matlab. (i)
13. Apply a numerical method (difference method) to find the numerical differentiation of a
function. (j)
14. *Produce a program for a numerical method (difference method) to find the numerical
differentiation of a function. (i)
15. *Apply a numerical method (Euler method, Runge- Kutta method) to solve a differential
equation. (j)
16. Produce a program for a numerical method (Euler method, Runge-Kutta method) to solve
a differential equation in Matlab. (i)