GRADUATE SCHOOL
M.SC. in Computer 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
|
|||||
| Course Type |
Service Course
|
|||||
| 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 |
X
|
| 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 | Accesses information in breadth and depth by conducting scientific research in Computer Engineering; evaluates, interprets and applies information. |
X | ||||
| 2 | Is well-informed about contemporary techniques and methods used in Computer Engineering and their limitations. | X | ||||
| 3 | Uses scientific methods to complete and apply information from uncertain, limited or incomplete data; can combine and use information from different disciplines. |
X | ||||
| 4 | Is informed about new and upcoming applications in the field and learns them whenever necessary. | X | ||||
| 5 | Defines and formulates problems related to Computer Engineering, develops methods to solve them and uses progressive methods in solutions. |
X | ||||
| 6 | Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs | X | ||||
| 7 | Designs and implements studies based on theory, experiments and modelling; analyses and resolves the complex problems that arise in this process. |
X | ||||
| 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. |
X | ||||
| 9 | Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale. |
X | ||||
| 10 | Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form. |
X | ||||
| 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. |
X | ||||
| 12 | Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity. |
X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest