# 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 N/A Bachelor’s Degree (First Cycle) Face To Face Lecture. 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. 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 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. 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 35 100

### Course Category

Core Courses X

### The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 Adequate knowledge in mathematics, science and subjects specific to the computer engineering discipline; the ability to apply theoretical and practical knowledge of these areas to complex engineering problems. X
2 The ability to identify, define, formulate and solve complex engineering problems; selecting and applying proper analysis and modeling techniques for this purpose. X
3 The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; the ability to apply modern design methods for this purpose. X
4 The ability to develop, select and utilize modern techniques and tools essential for the analysis and determination of complex problems in computer engineering applications; the ability to utilize information technologies effectively. X
5 The ability to design experiments, conduct experiments, gather data, analyze and interpret results for the investigation of complex engineering problems or research topics specific to the computer engineering discipline. X
6 The ability to work effectively in inter/inner disciplinary teams; ability to work individually X
7 Effective oral and writen communication skills in Turkish; the ability to write effective reports and comprehend written reports, to prepare design and production reports, to make effective presentations, to give and to receive clear and understandable instructions.
8 The knowledge of at least one foreign language; the ability to write effective reports and comprehend written reports, to prepare design and production reports, to make effective presentations, to give and to receive clear and understandable instructions.
9 Recognition of the need for lifelong learning; the ability to access information, to follow recent developments in science and technology.
10 The ability to behave according to ethical principles, awareness of professional and ethical responsibility;
11 Knowledge of the standards utilized in software engineering applications
12 Knowledge on business practices such as project management, risk management and change management;
13 Awareness about entrepreneurship, innovation
14 Knowledge on sustainable development
15 Knowledge on the effects of computer engineering applications on the universal and social dimensions of health, environment and safety;
16 Awareness of the legal consequences of engineering solutions
17 An ability to describe, analyze and design digital computing and representation systems. X
18 An ability to use appropriate computer engineering concepts and programming languages in solving computing problems. X

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