GRADUATE SCHOOL
M.SC. in Computer Engineering (With Thesis)
IE 520 | Course Introduction and Application Information
| Course Name |
Constraint Programming
|
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
|
IE 520
|
Fall/Spring
|
3
|
0
|
3
|
7.5
|
| Prerequisites |
None
|
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| Course Language |
English
|
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| Course Type |
Elective
|
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| Course Level |
Second Cycle
|
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| Mode of Delivery | - | |||||
| Teaching Methods and Techniques of the Course | - | |||||
| Course Coordinator | - | |||||
| Course Lecturer(s) | - | |||||
| Assistant(s) | - | |||||
| Course Objectives | The goal of this course is to have each student acquire the knowledge on a new programming paradigm based on constraints over finite domains and provide experience of how to use these methods for solving combinatorial problems. The Optimization Programming Language (OPL) and its Software Package will be introduced and used throughout the semester to model various combinatorial problems as constraint programs, as well as to write specific search methods. |
| Learning Outcomes |
The students who succeeded in this course;
|
| Course Description | The basic concepts of constraint programming. Modeling combinatorial problems in terms of constraints. Constraint consistency and propagation. Global constraints and their propagation algorithms. Search: construction of the search tree, exploration of the search tree, heuristics. Optimization. Advanced techniques: set variables, dealing with redundancy and symmetry. Implementation in a constraint programming language. |
|
|
Core Courses | |
| Major Area Courses | ||
| Supportive Courses | ||
| Media and Management Skills Courses | ||
| Transferable Skill Courses |
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
| Week | Subjects | Related Preparation |
| 1 | Constraint satisfaction problems | Lecture notes |
| 2 | Basic concepts of Constraint Programming | Lecture notes |
| 3 | Modelling combinatorial problem using OPL I | Lecture notes |
| 4 | Modelling combinatorial problem using OPL II | Lecture notes |
| 5 | Constraint consistency and propagation I | Lecture notes |
| 6 | Constraint consistency and propagation II | Lecture notes |
| 7 | Global constraints | Lecture notes |
| 8 | Ara sınav / Midterm | Lecture notes |
| 9 | Search heuristics I | Lecture notes |
| 10 | Search heuristics II | Lecture notes |
| 11 | Set variables | Lecture notes |
| 12 | Symmetry | Lecture notes |
| 13 | Optimisation problems | Lecture notes |
| 14 | Discussions, Research and Presentations I | Lecture notes |
| 15 | Discussions, Research and Presentations II | Lecture notes |
| 16 | Review of the Semester |
| Course Notes/Textbooks | Course slides |
| Suggested Readings/Materials | Related research papers |
EVALUATION SYSTEM
| Semester Activities | Number | Weigthing |
| Participation | ||
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques | ||
| Portfolio | ||
| Homework / Assignments | ||
| Presentation / Jury |
1
|
30
|
| Project | ||
| Seminar / Workshop | ||
| Oral Exams | ||
| Midterm |
1
|
30
|
| Final Exam |
1
|
40
|
| Total |
| Weighting of Semester Activities on the Final Grade |
60
|
|
| Weighting of End-of-Semester Activities on the Final Grade |
40
|
|
| Total |
ECTS / WORKLOAD TABLE
| Semester Activities | Number | Duration (Hours) | Workload |
|---|---|---|---|
| Theoretical Course Hours (Including exam week: 16 x total hours) |
16
|
3
|
48
|
| Laboratory / Application Hours (Including exam week: '.16.' x total hours) |
16
|
0
|
|
| Study Hours Out of Class |
15
|
6
|
90
|
| Field Work |
0
|
||
| Quizzes / Studio Critiques |
0
|
||
| Portfolio |
0
|
||
| Homework / Assignments |
0
|
||
| Presentation / Jury |
1
|
5
|
5
|
| Project |
0
|
||
| Seminar / Workshop |
0
|
||
| Oral Exam |
0
|
||
| Midterms |
1
|
15
|
15
|
| Final Exam |
1
|
22
|
22
|
| Total |
180
|
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
|
#
|
Program Competencies/Outcomes |
* Contribution Level
|
||||
|
1
|
2
|
3
|
4
|
5
|
||
| 1 | Accesses information in breadth and depth by conducting scientific research in Computer Engineering; evaluates, interprets and applies information. |
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| 2 | Is well-informed about contemporary techniques and methods used in Computer Engineering and their limitations. | |||||
| 3 | Uses scientific methods to complete and apply information from uncertain, limited or incomplete data; can combine and use information from different disciplines. |
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| 4 | Is informed about new and upcoming applications in the field and learns them whenever necessary. | |||||
| 5 | Defines and formulates problems related to Computer Engineering, develops methods to solve them and uses progressive methods in solutions. |
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| 6 | Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs | |||||
| 7 | Designs and implements studies based on theory, experiments and modelling; analyses and resolves the complex problems that arise in this process. |
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| 8 | Can work effectively in interdisciplinary teams as well as teams of the same discipline, can lead such teams and can develop approaches for resolving complex situations; can work independently and takes responsibility. |
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| 9 | Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale. |
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| 10 | Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form. |
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| 11 | Is knowledgeable about the social, environmental, health, security and law implications of Computer Engineering applications, knows their project management and business applications, and is aware of their limitations in Computer Engineering applications. |
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| 12 | Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity. |
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*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest