ECTS - Algorithms and Optimization Methods

Algorithms and Optimization Methods (SE328) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Algorithms and Optimization Methods SE328 Area Elective 3 0 0 3 5
Pre-requisite Course(s)
CMPE226
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 analyze and design algorithms and measure their complexities. In addition, students will be able to implement optimization methods for optimization problems.
Course Learning Outcomes The students who succeeded in this course;
  • Measure the complexity of algorithms
  • Analyze and design algorithms
  • Implement efficient algorithms for the solution of real life computational problems
  • Analyze, design and implement optimization methods
Course Content Design and analysis of algorithms; mathematical complexity of algorithms; master theorem; decrease-and-conquer; divide-and-conquer; transform-and-conquer; introduction to some optimization techniques; dynamic programming; greedy technique; iterative improvement; coping with limitations of algorithm power.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 The role of algorithms and Getting Started CLRS Ch 1-2
2 Growth of Functions, Asymptotic Notation CLRS Ch. 3
3 Solving Recurrences: Substitution Method CLRS Ch. 4
4 Solving Recurrences: Recursion-Tree Method, Master's Method CLRS Ch. 4
5 Brute Force and Exhaustive Search LVTN Ch. 3 & CLRS Ch. 22
6 Decrease-and-Conquer LVTN Ch. 4 & CLRS Ch. 22
7 Divide-and-Conquer LVTN Ch. 5 & CLRS Ch. 7
8 Transform-and-Conquer LVTN Ch. 6 & CLRS Ch. 6
9 Dynamic Programming LVTN Ch. 8 & CLRS Ch. 15
10 Dynamic Programming LVTN Ch. 8 & CLRS Ch. 15
11 Greedy Algorithms LVTN Ch. 9 & CLRS Ch. 16
12 Greedy Algorithms LVTN Ch. 9 & CLRS Ch. 16
13 Iterative Improvement: The Simplex Method LVTN Ch. 10
14 Limitations of Algorithm Power, Coping with the Limitations of Algorithm Power, P, NP, NP-Complete Problems LVTN Ch. 11
15 Final Exam
16 Final Exam

Sources

Course Book 1. Anany Levitin, Introduction to the Design & Analysis of Algorithms, 3rd edi-tion, Pearson, 2012.
Other Sources 2. T.H.Cormen, C.E.Leiserson, R.L.Rivest and C.Stein: Introduction to Algorithms, MIT Press 2001.
3. E.Horowitz, S.Sahni: Fundamentals of Computer Algorithms, Computer Sci-ence 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 Algo-rithms, Addison-Wesley Series in Computer Science and Information Pro-cessing, 1979.
7. S.S. Skiena, The Algorithm Design Manual, Springer – Verlag, New York, 1998.

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments 3 15
Presentation - -
Project - -
Report - -
Seminar - -
Midterms Exams/Midterms Jury 2 50
Final Exam/Final Jury 1 35
Toplam 6 100
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 Gains the ability to apply advanced computing and/or information knowledge in solving software engineering problems. X
2 Develops solutions using different technologies, software architectures and life-cycle approaches.
3 Gains the ability to design, implement, and evaluate a software system, component, process, or program using modern techniques and engineering tools for software engineering practices.
4 Gains ability to gather/acquire, analyze, interpret data and make decisions to understand software requirements.
5 Gains 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.
6 Gains the ability to access information to follow current developments in science and technology, conducts scientific research in the field of software engineering, and conducts a project.
7 Acquires an understanding of professional, legal, ethical and social issues and responsibilities related to Software Engineering.
8 Acquires project and risk management skills and gains awareness of the importance of entrepreneurship, innovation, and sustainable development, as well as international standards and methodologies.
9 Understands the impact of Software Engineering solutions in a global, environmental, societal and legal context while making decisions.
10 Gains awareness of the development, adoption, and ongoing support for the use of excellence standards in software engineering practices. X

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours)
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class 16 2 32
Presentation/Seminar Prepration
Project
Report
Homework Assignments 3 4 12
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury
Prepration of Final Exams/Final Jury 1 15 15
Total Workload 59