          Description of Individual Course Units
 Course Unit Code Course Unit Title Type of Course Unit Year of Study Semester Number of ECTS Credits İST206 MATRIX THEORY AND STATISTICS APPLICATIONS Compulsory 2 4 5
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
 1 To be able to understand matrix definition, types and operations. 2 To be able to solve systems of linear equations with matrix approach. 3 To be able to express some statistical information with matrices and to calculate mean, variance-covariance 4 To 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
 Week Theoretical Practice Laboratory 1 Definition and types of matrix Introduction to Matlab 2 Addition and multiplication in matrices, transpose of matrix Matlab applications 3 Inverse of the matrix, Moore-Penrose inverse Matlab applications 4 Fragmentation of matrices Matlab applications 5 Determinant Matlab applications 6 Linear independence, vector spaces Matlab applications 7 Rank concept Matlab applications 8 Midterm 9 Solution of linear equation systems with matrices Matlab applications 10 Eigenvalues and eigenvectors, inner product and Hermitian matrices Matlab applications 11 Matrix representations of some statistical information Matlab applications 12 Least squares method Matlab applications 13 Sample mean, covariance and correlation by matrix operations Matlab applications 14 Vector operations Matlab applications 15 Final exam
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 Activities Quantity Weight Midterm Examination 1 100 SUM 100 End Of Term (or Year) Learning Activities Quantity Weight Final Sınavı 1 100 SUM 100 Term (or Year) Learning Activities 40 End Of Term (or Year) Learning Activities 60 SUM 100
Work Placement(s) 