GRADUATE SCHOOL
M.SC. In Industrial Engineering (With Thesis)
CE 601 | Course Introduction and Application Information
| Course Name |
Advanced Algorithms
|
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
|
CE 601
|
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 |
Third 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 objective of this course is to introduce algorithms by looking at the real-world problems motivating them. Students will be taught a range of design and analysis techniques for problems that arise in computing applications. Greedy algorithms, divide and conquer type of algorithms and dynamic programming will be discussed within the context of different example applications. |
| Learning Outcomes |
The students who succeeded in this course;
|
| Course Description | The course covers basics of Algorithms Analysis, graph theoretic concepts, greedy algorithms, divide and conquer algorithms and dynamic programming algorithms. |
|
|
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 | Introduction: Some Representative Problems | Course Book; Chapter 1. |
| 2 | Basics of Algorithms Analysis | Course Book; Chapter 2. |
| 3 | Graphs | Course Book; Chapter 3. |
| 4 | Greedy Algorithms: Interval Scheduling | Course Book; Chapter 4. |
| 5 | Greedy Algorithms: Scheduling to Minimize Lateness | Course Book; Chapter 4. |
| 6 | Greedy Algorithms : Minimum-Cost Arborescences | Course Book; Chapter 4. |
| 7 | Divide and Conquer: Counting Inversions | Course Book; Chapter 5. |
| 8 | Midterm 1 | |
| 9 | Divide and Conquer: Integer Multiplication | Course Book; Chapter 5. |
| 10 | Divide and Conquer: Convolutions and The Fast Fourier Transform | Course Book; Chapter 5. |
| 11 | Dynamic Programming: Weighted Interval Scheduling | Course Book; Chapter 6. |
| 12 | Dynamic Programming: Subset Sums and Knapsacks | Course Book; Chapter 6. |
| 13 | Dynamic Programming: Sequence Alignment | Course Book; Chapter 6. |
| 14 | Midterm 2 | Course Book; Chapter 11. |
| 15 | Final Exam | |
| 16 | - |
| Course Notes/Textbooks | Algorithm Design, Jon Kleinberg, Éva Tardos, ISBN-10: 0321295358, ISBN-13: 9780321295354, Addison-Wesley, 2005. |
| Suggested Readings/Materials | Algorithms, Cormen, T.H., Liesersan, C.E. and Rivest, R.L. ISBN 0-01-013143-0, McGraw-Hill |
EVALUATION SYSTEM
| Semester Activities | Number | Weigthing |
| Participation | ||
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques | ||
| Portfolio | ||
| Homework / Assignments | ||
| Presentation / Jury | ||
| Project |
1
|
20
|
| Seminar / Workshop | ||
| Oral Exams | ||
| Midterm |
2
|
40
|
| Final Exam |
1
|
40
|
| Total |
| Weighting of Semester Activities on the Final Grade |
3
|
60
|
| Weighting of End-of-Semester Activities on the Final Grade |
1
|
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
|
4
|
60
|
| Field Work |
0
|
||
| Quizzes / Studio Critiques |
0
|
||
| Portfolio |
0
|
||
| Homework / Assignments |
0
|
||
| Presentation / Jury |
0
|
||
| Project |
1
|
25
|
25
|
| Seminar / Workshop |
0
|
||
| Oral Exam |
0
|
||
| Midterms |
2
|
25
|
50
|
| Final Exam |
1
|
42
|
42
|
| Total |
225
|
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
|
#
|
Program Competencies/Outcomes |
* Contribution Level
|
||||
|
1
|
2
|
3
|
4
|
5
|
||
| 1 | To have an appropriate knowledge of methodological and practical elements of the basic sciences and to be able to apply this knowledge in order to describe engineering-related problems in the context of industrial systems. |
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| 2 | To be able to identify, formulate and solve Industrial Engineering-related problems by using state-of-the-art methods, techniques and equipment. |
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| 3 | To be able to use techniques and tools for analyzing and designing industrial systems with a commitment to quality. |
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| 4 | To be able to conduct basic research and write and publish articles in related conferences and journals. |
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| 5 | To be able to carry out tests to measure the performance of industrial systems, analyze and interpret the subsequent results. |
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| 6 | To be able to manage decision-making processes in industrial systems. |
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| 7 | To have an aptitude for life-long learning; to be aware of new and upcoming applications in the field and to be able to learn them whenever necessary. |
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| 8 | To have the scientific and ethical values within the society in the collection, interpretation, dissemination, containment and use of the necessary technologies related to Industrial Engineering. |
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| 9 | To be able to design and implement studies based on theory, experiments and modeling; to be able to analyze and resolve the complex problems that arise in this process; to be able to prepare an original thesis that comply with Industrial Engineering criteria. |
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| 10 | To be able to follow information about Industrial Engineering in a foreign language; to be able to present 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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*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest