Course Information


Course Information
Course Title Code Semester L+U Hour Credits ECTS
ALGORITHMS COM3067 5. Semester 3 + 2 4.0 5.0

Prerequisites None

Language of Instruction English
Course Level Bachelor's Degree
Course Type Compulsory
Mode of delivery Lecture and laboratuary.
Course Coordinator
Instructors Murat OSMANOĞLU
Assistants Mert ÇALIŞ
Goals To teach students how to design and analyse algorithms and teach the advanced data structures.
Course Content Time and memory analysis of algorithms, Algorithm design methods: Brute force, divide-and-conquer, decrease-and-conquer, transfer-and-conquer. Discrete data structures. Advances nonlinear data strucrures: AVL trees, red-black trees, graph algorithms, greedy method, dynamic programming.
Learning Outcomes 1) Analysis the algorithms
2) Divides the problem into peaces.
3) uses nonlinear data structures.
4) Tries to find the best solution among several solutins.
5) Writes algorithms after determining the problem.
6) Programs the given algorithm.

Weekly Topics (Content)
Week Topics Teaching and Learning Methods and Techniques Study Materials
1. Week Introduction to algorithms. Lecture; Question Answer
Brainstorming
Problem Based Learning 		Presentation (Including Preparation Time)
2. Week Fundementals of algorithm analysis. Lecture; Question Answer
Brainstorming
Problem Based Learning 		Presentation (Including Preparation Time)
3. Week Recurences Lecture; Question Answer
Brainstorming
Problem Based Learning 		Presentation (Including Preparation Time)
4. Week Brute force. Lecture; Question Answer
Brainstorming
Problem Based Learning 		Presentation (Including Preparation Time)
5. Week Divide-and-conquer Lecture; Question Answer
Brainstorming
Problem Based Learning 		Presentation (Including Preparation Time)
6. Week Decrease and conquer Lecture; Question Answer
Brainstorming
Problem Based Learning 		Presentation (Including Preparation Time)
7. Week Problem solving tecniques Lecture; Question Answer; Problem Solving
Brainstorming
Problem Based Learning 		Presentation (Including Preparation Time)
8. Week review and exam. Lecture; Question Answer; Problem Solving
Brainstorming
Problem Based Learning 		Presentation (Including Preparation Time)
9. Week Transform and conquer Lecture; Question Answer; Problem Solving
Brainstorming
Problem Based Learning 		Presentation (Including Preparation Time)
10. Week Red-black trees Lecture; Question Answer; Problem Solving
Brainstorming
Problem Based Learning 		Presentation (Including Preparation Time)
11. Week Time and space trade-off Lecture; Question Answer; Problem Solving
Brainstorming
Problem Based Learning 		Presentation (Including Preparation Time)
12. Week Dynamic programming Lecture; Question Answer; Problem Solving
Brainstorming
Problem Based Learning 		Presentation (Including Preparation Time)
13. Week Greedy algorithms Lecture; Question Answer; Problem Solving
Brainstorming
Problem Based Learning 		Presentation (Including Preparation Time)
14. Week Problem solving tecniques 2 Lecture; Question Answer; Problem Solving
Brainstorming
Problem Based Learning 		Presentation (Including Preparation Time)
15. Week Project Question Answer
Opinion Pool
Problem Based Learning 		Presentation (Including Preparation Time)
16. Week Final Exam Question Answer; Problem Solving
Brainstorming
Problem Based Learning 		Homework

Sources Used in This Course
Recommended Sources
T. Cormen, C. Leiserson, R. Rivest, C. Stein, Introduction to Algorithms, Second Edition, The MIT Press, 2003
Anany Levitin, Introduction to the Design and Analysis of Algorithms, Addison Wesley; 2 edition, 2006

Assessment
Measurement and Evaluation Methods and Techniques
Exam, homework and practice results.
Relations with Education Attainment Program Course Competencies
Program RequirementsContribution LevelDK1DK2DK3DK4DK5DK6
PY15000000
PY25000000
PY35000000
PY45000000

*DK = Course's Contrubution.
0 1 2 3 4 5
Level of contribution None Very Low Low Fair High Very High
.

ECTS credits and course workload
Event Quantity Duration (Hour) Total Workload (Hour)
Course Duration (Total weeks*Hours per week) 14 3
Work Hour outside Classroom (Preparation, strengthening) 8 7
Homework 3 4
Practice (Teaching Practice, Music/Musical Instrument Practice , Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice) 4 2
Midterm Exam 1 2
Time to prepare for Midterm Exam 1 10
Final Exam 1 2
Time to prepare for Final Exam 1 5
1 15
1 15
14 2
Total Workload
Total Workload / 30 (s)
ECTS Credit of the Course
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Course Information
  • Course Information
  • Weekly Topics (Content)
  • Sources Used in This Course
  • Relations with Education Attainment Program Course Competencies
  • ECTS credits and course workload