Algorithms (CMPE323) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Algorithms CMPE323 3 0 0 3 7
Pre-requisite Course(s)
CMPE226
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, divide and conquer algorithms, dynamic programming, backtracking and branch and bound, lower bound theory, complexity of sorting and searching algorithms, graph algorithms, NP-hard and NP-complete problems, basic NPC problems, proving problems to be NPC, analysis of some string processing algorithms.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Introduction; Growth Functions Chapters 1.1, 1.2, 3.1, 3.2 (main text)
2 Analyzing Algorithms: Insertion Sort Chapters 2.1, 2.2
3 Analyzing Algorithms: Merge Sort, Recurrences (The Recursion-Tree Method) Chapter 2.3, 4 (introduction of chapter), 4.2
4 Analyzing Algorithms: Quicksort and Analysis of Quicksort Chapter 7.1, 7.2, 7.4, 5.2
5 Red-Black Trees Chapter 13.1, 13.2, 13.3
6 Dynamic Programming Chapter 15 (introduction of chapter), 15.1
7 Dynamic Programming Chapter 15.3, 15.2
8 Dynamic Programming Chapter 15.2 (cont.), 15.4
9 Greedy Algorithms Chapter 16.1, 16.2
10 Greedy Algorithms Chapter 16.2 (cont.), 16.3
11 Graph Algorithms Chapters 22.1, 22.2, 22.3
12 Graph Algorithms Chapters 22.4, 23 (introduction of chapter), 23.1, 23.2 (Kruskal)
13 Graph Algorithms Chapters 24 (introduction of chapter), 24.1, 24.2
14 Graph Algorithms, NP-Completeness Chapter 24.2 (cont.), 24.3, 34 (introduction of chapter)
15 Review -
16 Review

Sources

Course Book 1. T.H.Cormen, C.E.Leiserson, R.L.Rivest and C.Stein: Introduction to Algorithms, MIT Press 2001.
Other Sources 2. Anany Levitin, Introduction to the Design & Analysis of Algorithms, 3rd edition, Pearson, 2012.
3. E.Horowitz, S.Sahni: Fundamentals of Computer Algorithms, Computer Science Press, 1989
4. E.Horowitz, S.Sahni, S.Rajasekeran, Computer Algorithms, ISBN:  978-0-929306-41-4, Silicon Press, 2008.
5. J.Kleinberg, E.Tardos, Algorithm Design, Addison – Wesley, ISBN: 0-321-29535-8, 2006.
6. 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.
7. 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 3 10
Presentation - -
Project - -
Report - -
Seminar - -
Midterms Exams/Midterms Jury 2 50
Final Exam/Final Jury 1 35
Toplam 7 100
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 Ability to apply knowledge on Mathematics, Science and Engineering to advanced systems.
2 Implementing long-term research and development studies in major areas of Electrical and Electronics Engineering.
3 Ability to use modern engineering tools, techniques and facilities in design and other engineering applications.
4 Graduating researchers active on innovation and entrepreneurship.
5 Ability to report and present research results effectively.
6 Increasing the performance on accessing information resources and on following recent developments in science and technology.
7 An understanding of professional and ethical responsibility.
8 Increasing the performance on effective communications in both Turkish and English.
9 Increasing the performance on project management.
10 Ability to work successfully at project teams in interdisciplinary fields.

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 3 6 18
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 2 20 40
Prepration of Final Exams/Final Jury 1 20 20
Total Workload 174