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
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
10Solution of linear equation systems with matricesMatlab applications
11Eigenvalues and eigenvectors, inner product and Hermitian matricesMatlab applications
12Matrix representations of some statistical informationMatlab applications
13Least squares methodMatlab applications
14Sample mean, covariance and correlation by matrix operations Matlab applications
15Mean and variance of linear combinations of random variablesMatlab applications
16Final exam
Recommended or Required Reading
Planned Learning Activities and Teaching Methods
Assessment Methods and Criteria
Term (or Year) Learning ActivitiesQuantityWeight
SUM0
End Of Term (or Year) Learning ActivitiesQuantityWeight
SUM0
SUM0
Language of Instruction
Turkish
Work Placement(s)
Workload Calculation
ActivitiesNumberTime (hours)Total Work Load (hours)
Midterm Examination122
Final Examination122
Attending Lectures14342
Practice14114
Individual Study for Homework Problems166
Individual Study for Mid term Examination31030
Individual Study for Final Examination41040
TOTAL WORKLOAD (hours)136
Contribution of Learning Outcomes to Programme Outcomes
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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