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, variancecovariance  4  To be able to make matrix applications with computer programs. 

Mode of Delivery 
Face to Face 
Prerequisites and corequisities 

Recommended Optional Programme Components 

Course Contents 
Definition and types of matrices, addition and multiplication in matrices, transpose of matrix, inverse of matrix and MoorePenrose 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 

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, MoorePenrose 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   

Recommended or Required Reading 
1. Sabuncuoğlu Arif, Lineer CebirMühendislik 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  
Midterm Examination  1  100  SUM  100  
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)  

Workload Calculation 