ISTINYE UNIVERSITY BİLGİ PAKETİ / DERS KATALOĞU
Course Introduction and Application Information
| Course Code: | COE206 | ||||
| Course Name: | Analysis of Algorithms | ||||
| Semester: | Fall | ||||
| Course Credits: |
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| Language of instruction: | English | ||||
| Course Condition: | |||||
| Does the Course Require Work Experience?: | No | ||||
| Type of course: | Compulsory Courses | ||||
| Course Level: |
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| Mode of Delivery: | Face to face | ||||
| Course Coordinator: | Doç. Dr. AMIR SEYYEDABBASI | ||||
| Course Lecturer(s): | Assist. Prof. Dr. Muhammed Davud, Res. Assist. Yazım Beril Uluer | ||||
| Course Assistants: |
Course Objective and Content
| Course Objectives: | This course teaches students how to evaluate the efficiency and performance of algorithms. Students learn various techniques for analyzing algorithms, such as time complexity, space complexity, and asymptotic analysis. By studying these strategies and techniques, students learn how to choose the most appropriate approach for solving a given problem and how to analyze the performance of their solutions. |
| Course Content: | The course covers different types of algorithms such as sorting, searching and graph algorithms, as well as common data structures used in algorithm design. Students also learn about algorithmic strategies and techniques such as divide and conquer, reduce and conquer, transform and conquer, dynamic programming, greedy algorithms, and backtracking algorithms. |
Learning Outcomes
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The students who have succeeded in this course;
1) Evaluate the efficiency and performance of different algorithms using techniques such as time complexity, space complexity, and asymptotic analysis. 2) Apply various algorithmic strategies and techniques, such as divide and conquer, decrease and conquer, transform and conquer, dynamic programming, greedy algorithms, and backtracking algorithms, to solve different types of problems. 3) Use common data structures, such as arrays, linked lists, trees, and hash tables, to implement algorithms. 4) Analyze the performance of algorithmic solutions using empirical methods and theoretical analysis. |
Course Flow Plan
| Week | Subject | Related Preparation |
| 1) | Introduction | |
| 2) | Analysis of Algorithm Efficiency | |
| 3) | Analysis of Algorithm Efficiency | |
| 4) | Brute Force and Exhaustive Search | |
| 5) | Decrease-and-Conquer | |
| 6) | Divide-and-Conquer | |
| 7) | Divide-and-Conquer | |
| 8) | Midterm Exam | |
| 9) | Transform-and-Conquer | |
| 10) | Transform-and-Conquer | |
| 11) | Space and Time Trade-Offs | |
| 12) | Dynamic Programming | |
| 13) | Greedy Technique | |
| 14) | Iterative Improvement |
Sources
| Course Notes / Textbooks: | Introduction to the Design & Analysis of Algorithms - 3rd edition, by Anany Levitin |
| References: | Lecture notes |
Course - Program Learning Outcome Relationship
| Course Learning Outcomes | 1 |
2 |
3 |
4 |
|---|---|---|---|---|
| Program Outcomes |
Course - Learning Outcome Relationship
| No Effect | 1 Lowest | 2 Average | 3 Highest |
| Program Outcomes | Level of Contribution |
Assessment & Grading
| Semester Requirements | Number of Activities | Level of Contribution |
| Homework Assignments | 1 | % 20 |
| Midterms | 1 | % 30 |
| Final | 1 | % 50 |
| total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 50 | |
| PERCENTAGE OF FINAL WORK | % 50 | |
| total | % 100 | |
Workload and ECTS Credit Calculation
| Activities | Number of Activities | Workload |
| Course Hours | 13 | 39 |
| Study Hours Out of Class | 15 | 75 |
| Homework Assignments | 2 | 20 |
| Midterms | 1 | 2 |
| Final | 2 | 4 |
| Total Workload | 140 | |