| Course Name | Analysis of Algorithms |
| Code | Semester | Theory (hour/week) | Application/Lab (hour/week) | Local Credits | ECTS |
|---|---|---|---|---|---|
| CE 390 | Fall/Spring | 3 | 0 | 3 | 5 |
| Prerequisites |
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| Course Language | English | ||||||||
| Course Type | Elective | ||||||||
| Course Level | First Cycle | ||||||||
| Mode of Delivery | - | ||||||||
| Teaching Methods and Techniques of the Course | |||||||||
| Course Coordinator | - | ||||||||
| Course Lecturer(s) | - | ||||||||
| Assistant(s) | - | ||||||||
| Course Objectives | The objective of this course is to introduce algorithms by looking at the realworld problems motivating them. Students will be taught a range of design and analysis techniques for problems that arise in computing applications. Greedy algorithms, divideandconquer 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;
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| Course Description | Greedy algorithms, divideandconquer type of algorithms, dynamic programming and approximation algorithms. |
| Related Sustainable Development Goals | |
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| Core Courses | |
| Major Area Courses | X | |
| Supportive Courses | ||
| Media and Managment Skills Courses | ||
| Transferable Skill Courses |
| Week | Subjects | Required Materials |
| 1 | Introduction and motivation. Mathematical foundations, summation, recurrences and growth of functions | Cormen Chapter 2, 3, and 4 |
| 2 | Asymptotic notation and Master theorem | Cormen Chapter 4 |
| 3 | Binary heaps and analysis of heapsort | Cormen Chapter 6 |
| 4 | Sorting theory and more comparison sorting algorithms: Analysis of merge sort andQuicksort. | Cormen Chapter 7 |
| 5 | Worst case analysis of Quicksort | Cormen Chapter 7 |
| 6 | Sorting in linear time, lower bounds for sorting, counting sort, radix sort, bucket sort | Cormen Chapter 8 |
| 7 | Medians and order statistics. Finding median and rank in linear time, selectionalgorithm. | Cormen Chapter 9 |
| 8 | Midterm | |
| 9 | Elementary data structures and runtime analysis of insertion, deletion and update | Cormen Chapter 10 |
| 10 | Hash tables and runtime analysis. | Cormen Chapter 11 |
| 11 | Binary search trees and Redblack trees | Cormen Chapter 12 and 13 |
| 12 | Btrees and Augmenting data structures | Cormen Chapter 18 |
| 13 | Amortized analysis | Cormen Chapter 17 |
| 14 | Binomial heaps and fibonacci heaps | Cormen Chapter 19 and 20 |
| 15 | Review | |
| 16 | Review of the Semester |
| Course Notes/Textbooks | Introduction to Algorithms, 2/eThomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein, ISBN: 9780262533058, MIT PressData Structures and Algorithm Analysis in C++, Mark Allen Weiss, Addision Wesley, Third Edition. |
| Suggested Readings/Materials | Algorithm Design. Jon Kleinberg and Eva Tardos. 2006, Pearson Education, ISBN 0321372913 |
| Semester Activities | Number | Weigthing |
| Participation | ||
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques | ||
| Portfolio | ||
| Homework / Assignments | ||
| Presentation / Jury | ||
| Project | 6 | 30 |
| Seminar / Workshop | ||
| Oral Exam | ||
| Midterm | 1 | 30 |
| Final Exam | 1 | 40 |
| Total |
| Weighting of Semester Activities on the Final Grade | 60 | |
| Weighting of End-of-Semester Activities on the Final Grade | 40 | |
| Total |
| 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 | 60 |
| Field Work | |||
| Quizzes / Studio Critiques | |||
| Portfolio | |||
| Homework / Assignments | |||
| Presentation / Jury | |||
| Project | 6 | 2 | |
| Seminar / Workshop | |||
| Oral Exam | |||
| Midterms | 1 | 10 | |
| Final Exams | 1 | 20 | |
| Total | 150 |
| # | Program Competencies/Outcomes | * Contribution Level | ||||
1 | 2 | 3 | 4 | 5 | ||
| 1 | To be able to have a grasp of basic mathematics, applied mathematics or theories and applications of statistics. | |||||
| 2 | To be able to use advanced theoretical and applied knowledge, interpret and evaluate data, define and analyze problems, develop solutions based on research and proofs by using acquired advanced knowledge and skills within the fields of mathematics or statistics. | |||||
| 3 | To be able to apply mathematics or statistics in real life phenomena with interdisciplinary approach and discover their potentials. | |||||
| 4 | To be able to evaluate the knowledge and skills acquired at an advanced level in the field with a critical approach and develop positive attitude towards lifelong learning. | X | ||||
| 5 | To be able to share the ideas and solution proposals to problems on issues in the field with professionals, non-professionals. | X | ||||
| 6 | To be able to take responsibility both as a team member or individual in order to solve unexpected complex problems faced within the implementations in the field, planning and managing activities towards the development of subordinates in the framework of a project. | |||||
| 7 | To be able to use informatics and communication technologies with at least a minimum level of European Computer Driving License Advanced Level software knowledge. | |||||
| 8 | To be able to act in accordance with social, scientific, cultural and ethical values on the stages of gathering, implementation and release of the results of data related to the field. | |||||
| 9 | To be able to possess sufficient consciousness about the issues of universality of social rights, social justice, quality, cultural values and also environmental protection, worker's health and security. | X | ||||
| 10 | To be able to connect concrete events and transfer solutions, collect data, analyze and interpret results using scientific methods and having a way of abstract thinking. | |||||
| 11 | To be able to collect data in the areas of Mathematics or Statistics and communicate with colleagues in a foreign language. | |||||
| 12 | To be able to speak a second foreign language at a medium level of fluency efficiently. | |||||
| 13 | To be able to relate the knowledge accumulated throughout the human history to their field of expertise. | |||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
