Main Page     Information on the Institution     Degree Programs     General Information for Students     Türkçe  

 DEGREE PROGRAMS


 Associate's Degree (Short Cycle)


 Bachelor’s Degree (First Cycle)


 Master’s Degree (Second Cycle)

  Course Description
Course Name : Advanced Computer Programming Techniques

Course Code : EM-554

Course Type : Optional

Level of Course : Second Cycle

Year of Study : 1

Course Semester : Spring (16 Weeks)

ECTS : 6

Name of Lecturer(s) : Instructor İRFAN MACİT

Learning Outcomes of the Course : Acquires the ability to solve advanced engineering problems with the help of computer.
Gains the ability to develop advanced programming algorithm techniques.

Mode of Delivery : Face-to-Face

Prerequisites and Co-Prerequisites : None

Recommended Optional Programme Components : None

Aim(s) of Course : In addition to the knowledge students gain at the undergraduate level, this course aims to provide students with the ability to use advanced computer programming libraries, applications, and it is intended to emphasize the importance of academic studies. Theoretical and practical course is carried out in two sections. Basic programming knowledge to advanced students who have to bear the information, skills and knowledge areas and aims to develop methods to overcome the difficulties encountered in applications.

Course Contents : Methods used in the solution of engineering problems in basic computer programming courses. Numerical methods used in the analysis be solved with the help of computers, algorithms, development of the application of algorithm methods.

Language of Instruction : Turkish

Work Place : Laboratory


  Course Outline /Schedule (Weekly) Planned Learning Activities
Week Subject Student's Preliminary Work Learning Activities and Teaching Methods
1 Definitions of Basic Concepts C + + Programming. none Introduction to the programmimg tools
2 Basic Concepts and Advanced Computer Programming in C + +. Information about the distribution of project topics and project guide The selection and installation of computers, programming tools
3 Advanced Computer Introduction to Programming with C + + (continued), Algorithms and Flow Charts. Assignment about algorithm and flow diagrams. Giving information about computers, programming tools, environment settings, running the sample files
4 C + + Algorithms and applications of the principles of flow diagrams and program development Choosing project subject. Example applications with C + + Coding computers.
5 Examination of some of the solutions used in Industrial Engineering and C + + coding algorithms. Worksheet on MIP solution method Coding algorithms given in this course.
6 Examination of some of the solutions used in Industrial Engineering and C + + coding algorithms. Worksheet on LP solution method Coding algorithms given in this course.
7 Examination of some of the solutions used in Industrial Engineering and C + + coding algorithms. Worksheet on NLP solution method Coding algorithms given in this course.
8 Midterm Exam. none Practical exam.
9 Introduction to methods of distributed programming with C + + account. none Using libraries encoding
10 Introduction to methods of distributed programming with C + + accounts (continued), algorithms and applications. Assignments related to biomedical applications. The use of coding libraries, library application.
11 The use of C + + programming diagnostic biomedical application areas. none The use of source codes related to biomedical applications.
12 High-performance computing systems, and C + + libraries. GRID Computing Homework assigment. GRID libraries and source code analysis.
13 High-performance computing systems, and C + + libraries. Parallel Computing Homework assigment. Parallel Computing and examination of source code.
14 High-performance computing systems, and C + + libraries (cont.). none Parallel Computing and examination of source code.
15 An overview of issues and project presentations. none The project source code analysis.
16/17 Final Exam. none Practical exam.


  Required Course Resources
Resource Type Resource Name
Recommended Course Material(s)  
 
Required Course Material(s)


  Assessment Methods and Assessment Criteria
Semester/Year Assessments Number Contribution Percentage
    Mid-term Exams (Written, Oral, etc.) 1 60
    Homeworks/Projects/Others 8 40
Total 100
Rate of Semester/Year Assessments to Success 40
 
Final Assessments 100
Rate of Final Assessments to Success 60
Total 100

  Contribution of the Course to Key Learning Outcomes
# Key Learning Outcome Contribution*
1 Understand, interpret and apply knowledge in his/her field domain both in-depth and in-breadth by doing scientific research in industrial engineering. 3
2 Acquire comprehensive knowledge about methods and tools of industrial engineering and their limitations. 2
3 Work in multi-disciplinary teams and take a leading role and responsibility. 1
4 Identify, gather and use necessary information and data. 4
5 Complete and apply the knowledge by using scarce and limited resources in a scientific way and integrate the knowledge into various disciplines. 3
6 Keep up with the recent changes and applications in the field of Industrial Engineering and analyze these innovations when necessary. 2
7 Work in multi-disciplinary teams, take a leading role and responsibility and develop solutions for complex problems. 0
8 Analyze Industrial Engineering problems, develop innovative methods to solve the problems. 5
9 Have the ability to propose new and/or original ideas and methods in developing innovative solutions for designing systems, components or processes. 1
10 Design and perform analytical modeling and experimental research and analyze/solve complex matters emerged in this process. 4
11 Follow, study and learn new and developing applications of industrial engineering. 3
12 Use a foreign language in verbal and written communication at least B2 level of European Language Portfolio. 1
13 Present his/her research findings systematically and clearly in oral and written forms in national and international platforms. 0
14 Understand social and environmental implications of engineering practice. 0
15 Consider social, scientific and ethical values in the process of data collection, interpretation and announcement of the findings. 0
* Contribution levels are between 0 (not) and 5 (maximum).

  Student Workload - ECTS
Works Number Time (Hour) Total Workload (Hour)
Course Related Works
    Class Time (Exam weeks are excluded) 14 6 84
    Out of Class Study (Preliminary Work, Practice) 5 5 25
Assesment Related Works
    Homeworks, Projects, Others 8 5 40
    Mid-term Exams (Written, Oral, etc.) 1 6 6
    Final Exam 1 4 4
Total Workload: 159
Total Workload / 25 (h): 6.36
ECTS Credit: 6