ECTS - Advanced Algorithms
Advanced Algorithms (CMPE524) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Advanced Algorithms | CMPE524 | Area Elective | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | English |
|---|---|
| Course Type | Computer Engineering Elective Courses |
| Course Level | Natural & Applied Sciences Master's Degree |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture. |
| Course Lecturer(s) |
|
| Course Objectives | This course is designed to teach students how to analyse and design algorithms and measure their complexities. In addition, students will be able to develop efficient algorithms for the solution of real life computational problems. |
| Course Learning Outcomes |
The students who succeeded in this course;
|
| Course Content | Design and analysis of algorithms, O-notation, graph algorithms, topological sort, minimum spanning trees, single-shortest paths, all-pairs shortest paths, flow networks, NP-hard and NP-complete problems. |
Weekly Subjects and Releated Preparation Studies
| Week | Subjects | Preparation |
|---|---|---|
| 1 | Introduction: Growth of Functions | Chapters 1-3 (main text) |
| 2 | Introduction: Recurrences | Chapter 4 |
| 3 | Introduction: Sorting | Chapter 6-7 |
| 4 | Graphs, BFS, DFS | Chapter 22 |
| 5 | Topological Sort, Strongly Connected Components | Chapter 22 |
| 6 | Minimum Spanning Trees: Kruskall and Prim Algorithms | Chapter 23 |
| 7 | Single-Shortest Paths: Bellman-Ford Algorithm | Chapter 24 |
| 8 | Single-Shortest Paths: Dijkstra's Algorithm | Chapter 24 |
| 9 | All-Pairs Shortest Paths | Chapter 25 |
| 10 | Maximum-Flow: Flow networks | Chapter 26 |
| 11 | Maximum-Flow: Ford-Fulkerson's Algorithm | Chapter 26 |
| 12 | Maximum-Flow: Maximum Bipartite Matching | Chapter 26 |
| 13 | NP-Completeness | Chapter 34 |
| 14 | NP-Completeness | Chapter 34 |
| 15 | Review | |
| 16 | Review |
Sources
| Course Book | 1. T.H.Cormen, C.E.Leiserson, R.L.Rivest and C.Stein: Introduction to Algorithms, 2nd ed., MIT Press 2001. |
|---|---|
| Other Sources | 2. E.Horowitz, S.Sahni: Fundamentals of Computer Algorithms, Computer Science Press, 1989. |
| 3. E.Horowitz, S.Sahni, S.Rajasekeran, Computer Algorithms, ISBN: 978-0-929306-41-4, Silicon Press, 2008. | |
| 4. J.Kleinberg, E.Tardos, Algorithm Design, Addison – Wesley, ISBN: 0-321-29535-8, 2006. | |
| 5. A.V.Aho, J.E.Hopcroft, J.D.Ullman, The Design and Analysis of Computer Algorithms, Addison-Wesley Series in Computer Science and Information Processing, 1979. | |
| 6. S.S. Skiena, The Algorithm Design Manual, Springer – Verlag, New York, 1998. |
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | 1 | 5 |
| Laboratory | - | - |
| Application | - | - |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | 1 | 10 |
| Presentation | - | - |
| Project | - | - |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 2 | 50 |
| Final Exam/Final Jury | 1 | 35 |
| Toplam | 5 | 100 |
| Percentage of Semester Work | 65 |
|---|---|
| Percentage of Final Work | 35 |
| Total | 100 |
Course Category
| Core Courses | X |
|---|---|
| Major Area Courses | |
| Supportive Courses | |
| Media and Managment Skills Courses | |
| Transferable Skill Courses |
The Relation Between Course Learning Competencies and Program Qualifications
| # | Program Qualifications / Competencies | Level of Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| 1 | An ability to apply advanced knowledge of computing and/or informatics to solve software engineering problems. | X | ||||
| 2 | Develop solutions using different technologies, software architectures and life-cycle approaches. | X | ||||
| 3 | An ability to design, implement and evaluate a software system, component, process or program by using modern techniques and engineering tools required for software engineering practices. | X | ||||
| 4 | An ability to gather/acquire, analyze, interpret data and make decisions to understand software requirements. | X | ||||
| 5 | Skills of effective oral and written communication and critical thinking about a wide range of issues arising in the context of working constructively on software projects. | X | ||||
| 6 | An ability to access information in order to follow recent developments in science and technology and to perform scientific research or implement a project in the software engineering domain. | X | ||||
| 7 | An understanding of professional, legal, ethical and social issues and responsibilities related to Software Engineering. | |||||
| 8 | Skills in project and risk management, awareness about importance of entrepreneurship, innovation and long-term development, and recognition of international standards of excellence for software engineering practices standards and methodologies. | X | ||||
| 9 | An understanding about the impact of Software Engineering solutions in a global, environmental, societal and legal context while making decisions. | |||||
| 10 | Promote the development, adoption and sustained use of standards of excellence for software engineering practices. | X | ||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | 16 | 3 | 48 |
| Laboratory | |||
| Application | |||
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | 16 | 3 | 48 |
| Presentation/Seminar Prepration | |||
| Project | |||
| Report | |||
| Homework Assignments | 1 | 5 | 5 |
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 2 | 8 | 16 |
| Prepration of Final Exams/Final Jury | 1 | 15 | 15 |
| Total Workload | 132 | ||