FACULTY OF ENGINEERING

Department of Industrial Engineering

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
  CE 221 To succeed (To get a grade of at least DD)
Course Language
English
Course Type
Elective
Course Level
First Cycle
Mode of Delivery -
Teaching Methods and Techniques of the Course Problem Solving
Lecture / Presentation
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.
Learning Outcomes The students who succeeded in this course;
  • classify the different types of algorithms together with their purpose of use.
  • explain time and space complexity of different type of algorithms,
  • devise efficient greedy algorithms suitable to solve a particular computational problem,
  • implement efficient divide and conquer algorithms suitable to solve a particular computational problem,
  • formulate efficient dynamic programs suitable to solve a particular optimization problem.
Course Description 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 Semester Review
16 Final Exam

 

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
Portfolio
Homework / Assignments
1
30
Presentation / Jury
Project
Seminar / Workshop
Oral Exams
Midterm
1
30
Final 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
Theoretical Course Hours
(Including exam week: 16 x total hours)
16
3
48
Laboratory / Application Hours
(Including exam week: '.16.' x total hours)
16
0
Study Hours Out of Class
14
4
56
Field Work
0
Quizzes / Studio Critiques
0
Portfolio
0
Homework / Assignments
4
5
20
Presentation / Jury
0
Project
0
Seminar / Workshop
0
Oral Exam
0
Midterms
1
12
12
Final Exam
1
14
14
    Total
150

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1

To have adequate knowledge in Mathematics, Science and Industrial Engineering; to be able to use theoretical and applied information in these areas to model and solve Industrial Engineering problems.

X
2

To be able to identify, formulate and solve complex Industrial Engineering problems by using state-of-the-art methods, techniques and equipment; to be able to select and apply proper analysis and modeling methods for this purpose.

X
3

To be able to analyze a complex system, process, device or product, and to design with realistic limitations to meet the requirements using modern design techniques.

X
4

To be able to choose and use the required modern techniques and tools for Industrial Engineering applications; to be able to use information technologies efficiently.

X
5

To be able to design and do simulation and/or experiment, collect and analyze data and interpret the results for investigating Industrial Engineering problems and Industrial Engineering related research areas.

X
6

To be able to work efficiently in Industrial Engineering disciplinary and multidisciplinary teams; to be able to work individually.

X
7

To be able to communicate effectively in Turkish, both orally and in writing; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to present effectively; to be able to give and receive clear and comprehensible instructions

8

To have knowledge about contemporary issues and the global and societal effects of Industrial Engineering practices on health, environment, and safety; to be aware of the legal consequences of Industrial Engineering solutions.

X
9

To be aware of professional and ethical responsibility; to have knowledge of the standards used in Industrial Engineering practice.

X
10

To have knowledge about business life practices such as project management, risk management, and change management; to be aware of entrepreneurship and innovation; to have knowledge about sustainable development.

11

To be able to collect data in the area of Industrial Engineering; to be able to communicate with colleagues in a foreign language.

X
12

To be able to speak a second foreign at a medium level of fluency efficiently.

13

To recognize the need for lifelong learning; to be able to access information, to be able to stay current with developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Industrial Engineering.

X

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

 


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