FACULTY OF ENGINEERING

Department of Mechatronics Engineering

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
  CE 221 To succeed (To get a grade of at least DD)
Course Language
English
Course Type
Service Course
Course Level
First Cycle
Mode of Delivery -
Teaching Methods and Techniques of the Course Problem Solving
Lecture / Presentation
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;
  • 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 Description Greedy algorithms, divideandconquer type of algorithms, dynamic programming and approximation algorithms.

 



Course Category

Core Courses
Major Area Courses
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 Semester Review
16 Final Exam

 

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

Data Structures and Algorithm Analysis in C++, Mark Allen Weiss, Addision Wesley, Third Edition, 978-0132847377

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
Portfolio
Homework / Assignments
1
30
Presentation / Jury
Project
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
0
Study Hours Out of Class
15
4
60
Field Work
0
Quizzes / Studio Critiques
0
Portfolio
0
Homework / Assignments
4
3
12
Presentation / Jury
0
Project
0
Seminar / Workshop
0
Oral Exam
0
Midterms
1
10
10
Final Exam
1
20
20
    Total
150

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1

To have knowledge in Mathematics, science, physics knowledge based on mathematics; mathematics with multiple variables, differential equations, statistics, optimization and linear algebra; to be able to use theoretical and applied knowledge in complex engineering problems

2

To be able to identify, define, formulate, and solve complex mechatronics engineering problems; to be able to select and apply appropriate analysis and modeling methods for this purpose.

3

To be able to design a complex electromechanical system, process, device or product with sensor, actuator, control, hardware, and software to meet specific requirements under realistic constraints and conditions; to be able to apply modern design methods for this purpose.

4

To be able to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in Mechatronics Engineering applications; to be able to use information technologies effectively.

5

To be able to design, conduct experiments, collect data, analyze and interpret results for investigating Mechatronics Engineering problems.

6

To be able to work effectively in Mechatronics Engineering disciplinary and multidisciplinary teams; to be able to work individually.

7

To be able to communicate effectively in Turkish, both in oral and written forms; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, 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 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 solutions.

9

To be aware of ethical behavior, professional and ethical responsibility; information on standards used 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

Using a foreign language, he collects information about Mechatronics Engineering and communicates with his colleagues. ("European Language Portfolio Global Scale", Level B1)

12

To be able to use the second foreign language at intermediate level.

13

To recognize the need for lifelong learning; to be able to access information; to be able to follow developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Mechatronics Engineering.

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

 


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