Course Detail
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| ALGORITHM ANALYSIS | - | Spring Semester | 3+0 | 3 | 6 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | English |
| Course Level | First Cycle (Bachelor's Degree) |
| Course Type | Elective |
| Course Coordinator | Prof.Dr. Reda ALHAJJ |
| Name of Lecturer(s) | Prof.Dr. Reda ALHAJJ |
| Assistant(s) | |
| Aim | Introduce fundamental techniques for designing algorithms and analyzing the time and space requirements of these algorithms in a formal way. Mathematical background for algorithm analysis, sorting, searching, basic algorithms design and graph algorithms will be covered. |
| Course Content | This course contains; Week 1: Introduction: analysing algorithms, designing algorithms.,Week 2: Asymptotic Notation.,Week 3: Divide and Conquer Design Paradigm.,Week 4: Solving Recurrences.,Week 5: Analysis of Quicksort, Randomized Quicksort.,Week 6: Heapsort.,Week 7: Quicksort.,Week 8: Sorting in Linear Time.,Midterm,Week 10: Medians and Order Statistics.,Week 11: Dynamic Programming.,Week 12: Greedy Algorithms.,Week 13: Amortized Analysis, Dynamic Tables.,Week 13: Graphs, Breadth-first Search (BFS).. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| 1) At the end of this course the students will be able to describe the fundamentals of algorithm analysis. | 12, 14, 16, 9 | A, E |
| 2) At the end of this course the students will be able to construct complex algorithms using the data structures that they have learned. | 12, 14, 16, 9 | A, E |
| 3) At the end of this course the students will be able to develop complex algorithms and advanced data structures that are using trees and will be able to apply them to real world problems. | 10, 12, 14, 17, 9 | A, E, F |
| 4) At the end of this course the students will be able to develop complex algorithms and advanced data structures that are using graphs and will be able to apply them to real world problems. | 10, 12, 14, 17, 9 | A, E, F |
| 5) At the end of this course the students will be able to systematically look at a given computational problem and design a novel algorithm using techniques like dynamic programming, divide and conquer and greedy algorithms. | 12, 14, 16, 19, 9 | A, E |
| Teaching Methods: | 10: Discussion Method, 12: Problem Solving Method, 14: Self Study Method, 16: Question - Answer Technique, 17: Experimental Technique, 19: Brainstorming Technique, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, E: Homework, F: Project Task |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | Week 1: Introduction: analysing algorithms, designing algorithms. | Lecture Slides and textbook chapters 1 & 2 |
| 2 | Week 2: Asymptotic Notation. | Lecture Slides and textbook chapter 3 |
| 3 | Week 3: Divide and Conquer Design Paradigm. | Lecture Slides and textbook chapter 4 |
| 4 | Week 4: Solving Recurrences. | Lecture Slides and textbook chapter 4 |
| 5 | Week 5: Analysis of Quicksort, Randomized Quicksort. | Lecture Slides and textbook chapter 5 |
| 6 | Week 6: Heapsort. | Lecture Slides and textbook chapter 6 |
| 7 | Week 7: Quicksort. | Lecture Slides and textbook chapter 7 |
| 8 | Week 8: Sorting in Linear Time. | Lecture Slides and textbook chapter 8 |
| 9 | Midterm | Lecture Slides and textbook chapters from 1 to 9, inclusive. |
| 10 | Week 10: Medians and Order Statistics. | Lecture Slides and textbook chapter 9 |
| 11 | Week 11: Dynamic Programming. | Lecture Slides and textbook chapter 15 |
| 12 | Week 12: Greedy Algorithms. | Lecture Slides and textbook chapter 16 |
| 13 | Week 13: Amortized Analysis, Dynamic Tables. | Lecture Slides and textbook chapter 17 |
| 14 | Week 13: Graphs, Breadth-first Search (BFS). | Lecture Slides and textbook chapter 22 |
| Resources |
| T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms, Mit Press and McGraw-Hill, 2009. |
| The notes and the presentations will be delivered during the lectures. |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied knowledge in these areas in the solution of complex engineering problems. | X | |||||
| 2 | Ability to formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose. | X | |||||
| 3 | Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose. | X | |||||
| 4 | Ability to select and use modern techniques and tools needed for analyzing and solving complex problems encountered in engineering practice; ability to employ information technologies effectively. | X | |||||
| 5 | Ability to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or discipline specific research questions. | ||||||
| 6 | Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually. | X | |||||
| 7 | Ability to communicate effectively, both orally and in writing; knowledge of a minimum of one foreign language; ability to write effective reports and comprehend written reports, prepare design and production reports, make effective presentations, and give and receive clear and intelligible instructions. | X | |||||
| 8 | Awareness of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself. | X | |||||
| 9 | Knowledge on behavior according ethical principles, professional and ethical responsibility and standards used in engineering practices. | X | |||||
| 10 | Knowledge about business life practices such as project management, risk management, and change management; awareness in entrepreneurship, innovation; knowledge about sustainable development. | ||||||
| 11 | Knowledge about the global and social effects of engineering practices on health, environment, and safety, and contemporary issues of the century reflected into the field of engineering; awareness of the legal consequences of engineering solutions. | ||||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 30 | |
| Rate of Final Exam to Success | 70 | |
| Total | 100 | |
| ECTS / Workload Table | ||||||
| Activities | Number of | Duration(Hour) | Total Workload(Hour) | |||
| Course Hours | 14 | 3 | 42 | |||
| Guided Problem Solving | 14 | 4 | 56 | |||
| Resolution of Homework Problems and Submission as a Report | 6 | 5 | 30 | |||
| Term Project | 0 | 0 | 0 | |||
| Presentation of Project / Seminar | 0 | 0 | 0 | |||
| Quiz | 0 | 0 | 0 | |||
| Midterm Exam | 1 | 22 | 22 | |||
| General Exam | 1 | 22 | 22 | |||
| Performance Task, Maintenance Plan | 0 | 0 | 0 | |||
| Total Workload(Hour) | 172 | |||||
| Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(172/30) | 6 | |||||
| ECTS of the course: 30 hours of work is counted as 1 ECTS credit. | ||||||
Detail Informations of the Course
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| ALGORITHM ANALYSIS | - | Spring Semester | 3+0 | 3 | 6 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | English |
| Course Level | First Cycle (Bachelor's Degree) |
| Course Type | Elective |
| Course Coordinator | Prof.Dr. Reda ALHAJJ |
| Name of Lecturer(s) | Prof.Dr. Reda ALHAJJ |
| Assistant(s) | |
| Aim | Introduce fundamental techniques for designing algorithms and analyzing the time and space requirements of these algorithms in a formal way. Mathematical background for algorithm analysis, sorting, searching, basic algorithms design and graph algorithms will be covered. |
| Course Content | This course contains; Week 1: Introduction: analysing algorithms, designing algorithms.,Week 2: Asymptotic Notation.,Week 3: Divide and Conquer Design Paradigm.,Week 4: Solving Recurrences.,Week 5: Analysis of Quicksort, Randomized Quicksort.,Week 6: Heapsort.,Week 7: Quicksort.,Week 8: Sorting in Linear Time.,Midterm,Week 10: Medians and Order Statistics.,Week 11: Dynamic Programming.,Week 12: Greedy Algorithms.,Week 13: Amortized Analysis, Dynamic Tables.,Week 13: Graphs, Breadth-first Search (BFS).. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| 1) At the end of this course the students will be able to describe the fundamentals of algorithm analysis. | 12, 14, 16, 9 | A, E |
| 2) At the end of this course the students will be able to construct complex algorithms using the data structures that they have learned. | 12, 14, 16, 9 | A, E |
| 3) At the end of this course the students will be able to develop complex algorithms and advanced data structures that are using trees and will be able to apply them to real world problems. | 10, 12, 14, 17, 9 | A, E, F |
| 4) At the end of this course the students will be able to develop complex algorithms and advanced data structures that are using graphs and will be able to apply them to real world problems. | 10, 12, 14, 17, 9 | A, E, F |
| 5) At the end of this course the students will be able to systematically look at a given computational problem and design a novel algorithm using techniques like dynamic programming, divide and conquer and greedy algorithms. | 12, 14, 16, 19, 9 | A, E |
| Teaching Methods: | 10: Discussion Method, 12: Problem Solving Method, 14: Self Study Method, 16: Question - Answer Technique, 17: Experimental Technique, 19: Brainstorming Technique, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, E: Homework, F: Project Task |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | Week 1: Introduction: analysing algorithms, designing algorithms. | Lecture Slides and textbook chapters 1 & 2 |
| 2 | Week 2: Asymptotic Notation. | Lecture Slides and textbook chapter 3 |
| 3 | Week 3: Divide and Conquer Design Paradigm. | Lecture Slides and textbook chapter 4 |
| 4 | Week 4: Solving Recurrences. | Lecture Slides and textbook chapter 4 |
| 5 | Week 5: Analysis of Quicksort, Randomized Quicksort. | Lecture Slides and textbook chapter 5 |
| 6 | Week 6: Heapsort. | Lecture Slides and textbook chapter 6 |
| 7 | Week 7: Quicksort. | Lecture Slides and textbook chapter 7 |
| 8 | Week 8: Sorting in Linear Time. | Lecture Slides and textbook chapter 8 |
| 9 | Midterm | Lecture Slides and textbook chapters from 1 to 9, inclusive. |
| 10 | Week 10: Medians and Order Statistics. | Lecture Slides and textbook chapter 9 |
| 11 | Week 11: Dynamic Programming. | Lecture Slides and textbook chapter 15 |
| 12 | Week 12: Greedy Algorithms. | Lecture Slides and textbook chapter 16 |
| 13 | Week 13: Amortized Analysis, Dynamic Tables. | Lecture Slides and textbook chapter 17 |
| 14 | Week 13: Graphs, Breadth-first Search (BFS). | Lecture Slides and textbook chapter 22 |
| Resources |
| T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms, Mit Press and McGraw-Hill, 2009. |
| The notes and the presentations will be delivered during the lectures. |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied knowledge in these areas in the solution of complex engineering problems. | X | |||||
| 2 | Ability to formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose. | X | |||||
| 3 | Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose. | X | |||||
| 4 | Ability to select and use modern techniques and tools needed for analyzing and solving complex problems encountered in engineering practice; ability to employ information technologies effectively. | X | |||||
| 5 | Ability to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or discipline specific research questions. | ||||||
| 6 | Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually. | X | |||||
| 7 | Ability to communicate effectively, both orally and in writing; knowledge of a minimum of one foreign language; ability to write effective reports and comprehend written reports, prepare design and production reports, make effective presentations, and give and receive clear and intelligible instructions. | X | |||||
| 8 | Awareness of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself. | X | |||||
| 9 | Knowledge on behavior according ethical principles, professional and ethical responsibility and standards used in engineering practices. | X | |||||
| 10 | Knowledge about business life practices such as project management, risk management, and change management; awareness in entrepreneurship, innovation; knowledge about sustainable development. | ||||||
| 11 | Knowledge about the global and social effects of engineering practices on health, environment, and safety, and contemporary issues of the century reflected into the field of engineering; awareness of the legal consequences of engineering solutions. | ||||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 30 | |
| Rate of Final Exam to Success | 70 | |
| Total | 100 | |