Advanced Algorithms (CMPE524) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Advanced Algorithms CMPE524 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language English
Course Type N/A
Course Level Natural & Applied Sciences Master's Degree
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture.
Course Coordinator
Course Lecturer(s)
Course Assistants
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;
  • Analyze and design algorithms and measure their complexities
  • Recognize the theoretical foundations of the algorithms
  • Develop efficient algorithms for the solution of real life computational problems
  • Implement algorithms
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