CE 401 | Course Introduction and Application Information

Course Name
Algorithms Design
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
CE 401
Fall/Spring
3
0
3
5

Prerequisites
None
Course Language
English
Course Type
Elective
Course Level
First Cycle
Course Coordinator
Course Lecturer(s)
Assistant(s) -
Course Objectives The objective of this course is to introduce algorithms by looking at the real-world problems motivating them. Students will be taught a range of design and analysis techniques for problems that arise in computing applications. Greedy algorithms, divide and conquer type of algorithms and dynamic programming will be discussed within the context of different example applications. Approximation algorithms with an emphasis on load balancing and set cover problems will also be covered.
Course Description The students who succeeded in this course;
  • will be able to classify the different types of algorithms together with their purpose of use.
  • will be able to explain time and space complexity of different type of algorithms,
  • will be able to devise efficient greedy algorithms suitable to solve a particular computational problem,
  • will be able to implement efficient divide and conquer algorithms suitable to solve a particular computational problem,
  • will be able to formulate efficient dynamic programs suitable to solve a particular optimization problem.
Course Content The course covers basics of Algorithms Analysis, graph theoretic concepts, greedy algorithms, divide and conquer algorithms, dynamic programming, and approximation algorithms.

 



Course Category

Core Courses
Major Area Courses
X
Supportive Courses
Media and Management Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Related Preparation
1 Introduction: Some Representative Problems Course Book; Chapter 1.
2 Basics of Algorithms Analysis Course Book; Chapter 2.
3 Graphs Course Book; Chapter 3.
4 Greedy Algorithms: Interval Scheduling Course Book; Chapter 4.
5 Greedy Algorithms: Scheduling to Minimize Lateness Course Book; Chapter 4.
6 Greedy Algorithms : Minimum-Cost Arborescences Course Book; Chapter 4.
7 Divide and Conquer: Counting Inversions Course Book; Chapter 5.
8 Midterm
9 Divide and Conquer: Integer Multiplication Course Book; Chapter 5.
10 Divide and Conquer: Convolutions and The Fast Fourier Transform Course Book; Chapter 5.
11 Dynamic Programming: Weighted Interval Scheduling Course Book; Chapter 6.
12 Dynamic Programming: Subset Sums and Knapsacks Course Book; Chapter 6.
13 Dynamic Programming: Sequence Alignment Course Book; Chapter 6.
14 Approximation Algorithms: Load Balancing Course Book; Chapter 11.
15 Approximation Algorithms: Set Cover Course Book; Chapter 11.
16 Review of the Semester  

 

Course Notes/Textbooks Algorithm Design, Jon Kleinberg, Éva Tardos, ISBN-10: 0321295358, ISBN-13: 9780321295354, Addison-Wesley, 2005.
Suggested Readings/Materials Algorithms, Cormen, T.H., Liesersan, C.E. and Rivest, R.L. ISBN 0-01-013143-0, McGraw-Hill

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Homework / Assignments
Presentation / Jury
Project
Seminar / Workshop
Portfolios
Midterms / Oral Exams
2
60
Final / Oral Exam
1
40
Total

Weighting of Semester Activities on the Final Grade
2
60
Weighting of End-of-Semester Activities on the Final Grade
1
40
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Course Hours
Including exam week: 16 x total hours
16
3
48
Laboratory / Application Hours
Including exam week: 16 x total hours
16
Study Hours Out of Class
15
4
Field Work
Quizzes / Studio Critiques
Homework / Assignments
Presentation / Jury
Project
Seminar / Workshop
Portfolios
Midterms / Oral Exams
2
13
Final / Oral Exam
1
16
    Total
150

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1 Adequate knowledge in Mathematics, Science and Software Engineering; ability to use theoretical and applied information in these areas to model and solve Software Engineering problems X
2 Ability to identify, define, formulate, and solve complex Software Engineering problems; ability to select and apply proper analysis and modeling methods for this purpose X
3 Ability to design, implement, verify, validate, measure and maintain a complex software system, process or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern methods for this purpose X
4 Ability to devise, select, and use modern techniques and tools needed for Software Engineering practice
5 Ability to design and conduct experiments, gather data, analyze and interpret results for investigating Software Engineering problems
6 Ability to work efficiently in Software Engineering disciplinary and multi-disciplinary teams; ability to work individually
7 Ability to communicate effectively in Turkish, both orally and in writing; knowledge of a minimum of two foreign languages
8 Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself
9 Awareness of professional and ethical responsibility
10 Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development
11 Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of Software Engineering solutions

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest