Description of Individual Course Units
Course Unit CodeCourse Unit TitleType of Course UnitYear of StudySemesterNumber of ECTS Credits
İST206MATRIX THEORY AND STATISTICS APPLICATIONSCompulsory245
Level of Course Unit
First Cycle
Language of Instruction
Turkish
Objectives of the Course
To understand the definition of matrix, its types and properties, to solve systems of equations with matrix approach, to express some statistical information with matrices and to show its application with package programs.
Name of Lecturer(s)
Doç.Dr. Halil TANIL
Learning Outcomes
1To be able to understand matrix definition, types and operations.
2To be able to solve systems of linear equations with matrix approach.
3To be able to express some statistical information with matrices and to calculate mean, variance-covariance
4To be able to make matrix applications with computer programs.
Mode of Delivery
Face to Face
Prerequisites and co-requisities
Recommended Optional Programme Components
Course Contents
Definition and types of matrices, addition and multiplication in matrices, transpose of matrix, inverse of matrix and Moore-Penrose inverse, fragmentation of matrices, determinant, linear independence, vector spaces, rank concept, solution of linear equation systems with matrices, eigenvalues and eigenvectors, inner product and Hermian matrices matrix representations of some statistical information, least squares method, sample mean with matrix operations, covariance and correlation calculation, mean and variance of linear combinations of random variables, etc. topics.
Weekly Detailed Course Contents
WeekTheoreticalPracticeLaboratory
1Definition and types of matrixIntroduction to Matlab
2Addition and multiplication in matrices, transpose of matrixMatlab applications
3Inverse of the matrix, Moore-Penrose inverseMatlab applications
4Fragmentation of matricesMatlab applications
5DeterminantMatlab applications
6Linear independence, vector spacesMatlab applications
7Rank conceptMatlab applications
8Midterm
9Solution of linear equation systems with matricesMatlab applications
10Eigenvalues and eigenvectors, inner product and Hermitian matricesMatlab applications
11Matrix representations of some statistical informationMatlab applications
12Least squares methodMatlab applications
13Sample mean, covariance and correlation by matrix operations Matlab applications
14Vector operationsMatlab applications
15Final exam
Recommended or Required Reading
1. Sabuncuoğlu Arif, Lineer Cebir-Mühendislik ve İstatistik Bölümleri İçin, Nobel Yayın Dağıtım. (2012) 2. Kolman B. And Hill D.R., Elementary Linear Algebra with Applications. Pearson Education, Inc. (2008)
Planned Learning Activities and Teaching Methods
Activities are given in detail in the sections of "Assessment Methods and Criteria" and "Workload Calculation".
Assessment Methods and Criteria
Term (or Year) Learning ActivitiesQuantityWeight
Midterm Examination1100
SUM100
End Of Term (or Year) Learning ActivitiesQuantityWeight
Final Sınavı1100
SUM100
Term (or Year) Learning Activities40
End Of Term (or Year) Learning Activities60
SUM100
Work Placement(s)
Workload Calculation
ActivitiesNumberTime (hours)Total Work Load (hours)
Midterm Examination122
Final Examination122
Attending Lectures14456
Individual Study for Mid term Examination11515
Individual Study for Final Examination12020
Reading14342
TOTAL WORKLOAD (hours)137
Contribution of Learning Outcomes to Programme Outcomes
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LO1444 4 4  5  44 4 5     4
LO2454 4 4  5  44 4 5     4
LO3554 4 5  5  44 4 5     4
LO4444 4 5  5  44 5 5     4
* Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High
 
Ege University, Bornova - İzmir / TURKEY • Phone: +90 232 311 10 10 • e-mail: intrec@mail.ege.edu.tr