PAÜ .:. Eğitim Öğretim Bilgi Sistemi

COURSE INFORMATION

Course Code	Course Title	L+P Hour	Semester	ECTS
YBS 207	SCIENTIFIC COMPUTING	3 + 1	3rd Semester	5

COURSE DESCRIPTION

Course Level	Bachelor's Degree
Course Type	Elective
Course Objective	Teaching mathematics infrastructures to students for Optimization and Artificial Neural Networks courses with using MATLAB.
Course Content	Introduction to MATLAB, approximate computing of the roots of the system with single variable, matrices and linear systems, solution methods for linear and nonlinear systems, curve-fitting in linear systems, interpolation, numerical derivation and integration.
Prerequisites	No the prerequisite of lesson.
Corequisite	No the corequisite of lesson.
Mode of Delivery	Face to Face

COURSE LEARNING OUTCOMES

1	Uses MATLAB in basic form.
2	Understands iterative methods for linear and nonlinear systems with single variable.
3	Knows vector, norm, rank, eigenvalue, eigenvector concepts and applies in MATLAB.
4	Knows solution methods of linear systems and uses MATLAB for this purpose.
5	Understands solution methods of nonlinear systems and uses MATLAB for this purpose.
6	Knows curve-fitting methods for linear systems and applies in MATLAB.
7	Knows interpolation polynomials.
8	Understands numerical derivation and integration concepts and uses MATLAB for this purpose

COURSE'S CONTRIBUTION TO PROGRAM

	PO 01	PO 02	PO 03	PO 04	PO 05	PO 06	PO 07	PO 08	PO 09	PO 10	PO 11	PO 12
LO 001							4	5	4	4	3	4
LO 002							4	4	4	3	4	4
LO 003							3	5	5	3	4	4
LO 004							4	4	3	4	3	4
LO 005							5	5	4	4	3	4
LO 006							4	5	4	4	4	3
LO 007							5	4	4	3	5	4
LO 008							4	3	4	5	5	4
Sub Total							33	35	32	30	31	31
Contribution	0	0	0	0	0	0	4	4	4	4	4	4

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION

Activities	Quantity	Duration (Hour)	Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)	14	4	56
Hours for off-the-classroom study (Pre-study, practice)	14	3	42
Assignments	3	4	12
Mid-terms	1	8	8
Final examination	1	12	12
Total Work Load ECTS Credit of the Course			130 5

COURSE DETAILS

Select Year

	Course Term	No	Instructors
Details	2017-2018 Fall	2	BEDRİ BAHTİYAR

Course Details

Course Code	Course Title	L+P Hour	Course Code	Language Of Instruction	Course Semester
YBS 207	SCIENTIFIC COMPUTING	3 + 1	2	Turkish	2017-2018 Fall

Course Coordinator	E-Mail	Phone Number	Course Location	Attendance
Asts. Prof. Dr. BEDRİ BAHTİYAR	bedribahtiyar@pau.edu.tr		İİBF C0012	%70

Goals		Teaching mathematics infrastructures to students for Optimization and Artificial Neural Networks courses with using MATLAB.
Content		Introduction to MATLAB, approximate computing of the roots of the system with single variable, matrices and linear systems, solution methods for linear and nonlinear systems, curve-fitting in linear systems, interpolation, numerical derivation and integration.

Topics

Weeks	Topics
1	Introduction to MATLAB
2	Understands iterative methods for linear and nonlinear systems with single variable
3	Understands iterative methods for linear and nonlinear systems with single variable
4	Knows vector, norm, rank, eigenvalue, eigenvector concepts and applies in MATLAB
5	Knows solution methods of linear systems and uses MATLAB for this purpose
6	Knows solution methods of linear systems and uses MATLAB for this purpose
7	Understands solution methods of nonlinear systems and uses MATLAB for this purpose
8	Understands solution methods of nonlinear systems and uses MATLAB for this purpose
9	Understands solution methods of nonlinear systems and uses MATLAB for this purpose
10	Knows curve-fitting methods for linear systems and applies in MATLAB
11	Knows curve-fitting methods for linear systems and applies in MATLAB
12	Knows interpolation polynomials
13	Understands numerical derivation concepts and uses MATLAB for this purpose
14	Understands numerical integration concepts and uses MATLAB for this purpose

Materials

Materials are not specified.

Resources

Course Assessment

Assesment Methods	Percentage (%)	Assesment Methods Title
Final Exam	60	Final Exam
Midterm Exam	40	Midterm Exam

L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes

Prospective Student