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
Portfolios
Midterms / Oral Exams
1
30
Final / Oral 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
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
Portfolios
Midterms / Oral Exams
1
10
Final / Oral Exam
1
20
    Total
150

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1 Adequate knowledge in Mathematics, Science and Software Engineering; ability to use theoretical and applied information in these areas to model and solve Software Engineering problems X
2 Ability to identify, define, formulate, and solve complex Software Engineering problems; ability to select and apply proper analysis and modeling methods for this purpose X
3 Ability to design, implement, verify, validate, measure and maintain a complex software system, process or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern methods for this purpose X
4 Ability to devise, select, and use modern techniques and tools needed for Software Engineering practice
5 Ability to design and conduct experiments, gather data, analyze and interpret results for investigating Software Engineering problems
6 Ability to work efficiently in Software Engineering disciplinary and multi-disciplinary teams; ability to work individually
7 Ability to communicate effectively in Turkish, both orally and in writing; knowledge of a minimum of two foreign languages
8 Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself
9 Awareness of professional and ethical responsibility
10 Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development
11 Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of Software Engineering solutions

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