CE 390 | Course Introduction and Application Information

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
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 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.
Course Description The students who succeeded in this course;
  • will be able to analyze the time and space complexity of algorithms,
  • will be able to efficiently solve suitable problems with greedy algorithms,
  • will be able to discuss if a problem could be solved with divide and conquer algorithm and solve suitable problems with divide and conquer algorithm,
  • will be able to discuss if a problem could be solved with a dynamic programming algorithm and solve suitable problems with dynamic programming algorithms,
  • will be able to compare the trade-off between the time complexity and the optimality of the solution to find the most optimal solution and discuss approximation algorithms when the optimal is not feasible.
Course Content Greedy algorithms, divideandconquer type of 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 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

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Homework / Assignments
Presentation / Jury
Project
6
30
Seminar / Workshop
Oral Exams
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

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
Study Hours Out of Class
15
4
Field Work
Quizzes / Studio Critiques
Homework / Assignments
Presentation / Jury
Project
6
2
Seminar / Workshop
Oral Exam
Midterms
1
10
Final Exam
1
20
    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, Computer Science and Software Engineering; to be able to use theoretical and applied information in these areas on complex engineering problems.

X
2

To be able to identify, define, formulate, and solve complex Software Engineering problems; to be able to select and apply proper analysis and modeling methods for this purpose.

X
3

To be able to design, implement, verify, validate, document, measure and maintain a complex software system, process, or product under realistic constraints and conditions, in such a way as to meet the requirements; ability to apply modern methods for this purpose.

X
4

To be able to devise, select, and use modern techniques and tools needed for analysis and solution of complex problems in software engineering applications; to be able to use information technologies effectively.

X
5

To be able to design and conduct experiments, gather data, analyze and interpret results for investigating complex Software Engineering problems.

X
6

To be able to work effectively in Software Engineering disciplinary and multi-disciplinary teams; to be able to work individually.

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 be able to present effectively, to be able to give and receive clear and comprehensible instructions.

8

To have knowledge about global and social impact of engineering practices and software applications on health, environment, and safety; to have knowledge about contemporary issues as they pertain to engineering; to be aware of the legal ramifications of Engineering and Software Engineering solutions.

9

To be aware of ethical behavior, professional and ethical responsibility; to have knowledge about standards utilized in engineering applications.

10

To have knowledge about industrial practices such as project management, risk management, and change management; to have awareness of entrepreneurship and innovation; to have knowledge about sustainable development.

11

To be able to collect data in the area of Software Engineering, and to be able to 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 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 Software Engineering.

X

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