Self-routing autonomous robots by IUE engineers
An autonomous robot that can re-route using artificial intelligence, when it encounters an obstacle, has been developed with the project ...
Course Name |
Data Structures and Algorithms I
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Code
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Semester
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Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
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ECTS
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CE 221
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Fall/Spring
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3
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2
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4
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7
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Prerequisites |
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Course Language |
English
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Course Type |
Elective
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Course Level |
First Cycle
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Mode of Delivery | - | |||||||
Teaching Methods and Techniques of the Course | Problem SolvingApplication: Experiment / Laboratory / WorkshopLecture / Presentation | |||||||
National Occupation Classification | - | |||||||
Course Coordinator | ||||||||
Course Lecturer(s) | ||||||||
Assistant(s) |
Course Objectives | The objective of this course is to teach students the notion of an abstract data type (ADT) which is central to the design and analysis of computer algorithms. This course introduces abstract data types, and presents algorithms and data structures for implementing several ADTs. It emphasizes the efficiency of algorithms as evaluated by asymptotic analysis of running time. The programming assignments will be given in the programming languages taught in SE 115 and/or SE116. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning Outcomes |
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Course Description | Algorithm analysis, linear data structures, trees, hashing, priority queues, sorting, and graph algorithms. |
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Core Courses | |
Major Area Courses | ||
Supportive Courses | ||
Media and Management Skills Courses | ||
Transferable Skill Courses |
Week | Subjects | Related Preparation | Learning Outcome |
1 | Introduction: Mathematics Review and Recursion | M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 1.1, 1.2, 1.3) | |
2 | Algorithm Analysis (basic concepts of algorithms, modeling runtimes, recurrences, Big-Oh notations, Running Time Calculations) | M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 2.1, 2.2, 2.3) | |
3 | Algorithm Analysis and Linear Data Structures: (Linked Lists) | M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 2.4, 3.1 - 3.5) | |
4 | Linear Data Structures (Linked Lists, Stacks, Stack Applications) | M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 3.5, 3.6) | |
5 | Linear Data Structures (Queues) and Trees (Binary trees) | M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 3.7, 4.1, 4.2) | |
6 | Trees (Binary search trees) | M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 4.3) | |
7 | Trees (AVL Trees) | M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 4.4) | |
8 | Midterm | ||
9 | Hashing | M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 5.1 – 5.5) | |
10 | Priority Queues: Binary Heaps | M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 6.1, 6.2, 6.3) | |
11 | Sorting (Insertion Sort, Shellsort, Heapsort) | M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 7.1, 7.2, 7.3, 7.4, 7.5) | |
12 | Sorting (Mergesort, Quicksort) | M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 7.6, 7.7) | |
13 | Graph Algorithms (Definitions, Representation, Topological Sort) | M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 9.1 - 9.2) | |
14 | Graph Algorithms (Shortest Path Algorithms) | M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 9.3) | |
15 | Semester Review | ||
16 | Final Exam |
Course Notes/Textbooks | M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012, 978-0132576277 |
Suggested Readings/Materials | R. Sedgewick, K. Wayne, Algorithms, 4/e, Addison-Wesley Professional, 2011, 978-0321573513 |
Semester Activities | Number | Weigthing | LO 1 | LO 2 | LO 3 | LO 4 | LO 5 | LO 6 | LO 7 | LO 8 |
Participation | ||||||||||
Laboratory / Application |
8
|
40
|
||||||||
Field Work | ||||||||||
Quizzes / Studio Critiques | ||||||||||
Portfolio | ||||||||||
Homework / Assignments | ||||||||||
Presentation / Jury | ||||||||||
Project | ||||||||||
Seminar / Workshop | ||||||||||
Oral Exams | ||||||||||
Midterm |
1
|
20
|
||||||||
Final Exam |
1
|
40
|
||||||||
Total |
Weighting of Semester Activities on the Final Grade |
2
|
60
|
Weighting of End-of-Semester Activities on the Final Grade |
1
|
40
|
Total |
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
|
2
|
32
|
Study Hours Out of Class |
14
|
3
|
42
|
Field Work |
0
|
||
Quizzes / Studio Critiques |
0
|
||
Portfolio |
0
|
||
Homework / Assignments |
1
|
40
|
40
|
Presentation / Jury |
0
|
||
Project |
0
|
||
Seminar / Workshop |
0
|
||
Oral Exam |
0
|
||
Midterms |
1
|
24
|
24
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Final Exam |
1
|
24
|
24
|
Total |
210
|
#
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PC Sub | Program Competencies/Outcomes |
* Contribution Level
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1
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2
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3
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4
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5
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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 |
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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. |
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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. |
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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. |
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5 |
To be able to design, conduct experiments, collect data, analyze and interpret results for investigating Mechatronics Engineering problems. |
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6 |
To be able to work effectively in Mechatronics Engineering disciplinary and multidisciplinary teams; to be able to work individually. |
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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. |
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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. |
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9 |
To be aware of ethical behavior, professional and ethical responsibility; information on standards used in engineering applications. |
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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. |
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11 |
Using a foreign language, he collects information about Mechatronics Engineering and communicates with his colleagues. ("European Language Portfolio Global Scale", Level B1) |
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12 |
To be able to use the second foreign language at intermediate level. |
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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. |
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*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
An autonomous robot that can re-route using artificial intelligence, when it encounters an obstacle, has been developed with the project ...
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