Advanced Algorithms (CMPE524) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Advanced Algorithms CMPE524 Elective Courses 3 0 0 3 5
Pre-requisite Course(s)
Course Language English
Course Type Elective Courses
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


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 knowledge of mathematics, science, and engineering. X
2 An ability to design and conduct experiments, as well as to analyse and interpret data.
3 An ability to design a system, component, or process to meet desired needs. X
4 An ability to function on multi-disciplinary domains.
5 An ability to identify, formulate, and solve engineering problems. X
6 An understanding of professional and ethical responsibility.
7 An ability to communicate effectively.
8 Recognition of the need for, and an ability to engage in life-long learning.
9 A knowledge of contemporary issues.
10 An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice. X
11 Skills in project management and recognition of international standards and methodologies X
12 An ability to produce engineering products or prototypes that solve real-life problems. X
13 Skills that contribute to professional knowledge. X
14 An ability to make methodological scientific research.
15 An ability to produce, report and present an original or known scientific body of knowledge.
16 An ability to defend an originally produced idea.

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 16 3 48
Special Course Internship
Field Work
Study Hours Out of Class 16 3 48
Presentation/Seminar Prepration
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