Innovative Algorithms(IT 542)
| Course Code | Course Name | Semester | Theory | Practice | Lab | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| IT 542 | Innovative Algorithms | 2 | 4 | 0 | 0 | 3 | 8 |
| Prerequisites | |
| Admission Requirements |
| Language of Instruction | English |
| Course Type | Compulsory |
| Course Level | Masters Degree |
| Course Instructor(s) | Gülfem ALPTEKİN gulfem@gmail.com (Email) Pınar ULUER puluer@gsu.edu.tr (Email) |
| Assistant | |
| Objective | The aim of this course is to teach students the fundamental concepts, types, and applications of algorithms, and to develop their skills in algorithm design and analysis. The course seeks to bridge the gap between algorithm theory and practical applications. Students will understand when and for what purposes algorithms should be used, and will be able to identify the appropriate class of algorithms to solve a given problem. Another objective is to raise students’ awareness of the social impacts of algorithms and their potential biases or unfairness. |
| Content |
1. Fundamentals of algorithms, their role in everyday life, history, types of algorithms, definition of execution time, computational complexity. 2. Algorithm complexity analysis, time and space complexity, best, average, and worst-case analyses with examples. 3. Basic algorithms and decision-making mechanisms (randomized algorithms, sorting and searching algorithms, best-first search, A*, minimax algorithm). 4. Fundamental graph and shortest path algorithms, minimum spanning tree algorithms (traversal algorithms, Dijkstra, Bellman-Ford, Floyd-Warshall). 5 Heuristic and metaheuristic algorithms (local search, simulated annealing, tabu search). 6. Evolutionary algorithms (genetic algorithm, particle swarm optimization, ant colony optimization, artificial bee algorithm). 7. Dynamic programming, greedy algorithms. 8. Midterm exam. 9. Planning algorithms: basic decision theory, game theory, reinforcement learning, decision-making under uncertainty. 10. Algorithm ethics, fairness, and open problems. 11. Student projects. |
| Course Learning Outcomes |
Students who successfully complete this course will acquire the following skills: 1. Analyze the fundamental principles of algorithms in terms of time and space efficiency used in algorithm analysis. 2. Develop the ability to understand, implement, and determine when to use a wide range of algorithms. 3. Gain in-depth knowledge of evolutionary algorithms and learn how to apply them to solve real-world problems. 4. Acquire knowledge of current and innovative algorithms, and develop the ability to understand them and engage in informed discussions about them. 5. Gain awareness of algorithm ethics and bias, and develop the ability to consider these issues when designing and implementing technological solutions. |
| Teaching and Learning Methods | Theoretical and practical instruction, classroom lectures, interactive question–answer sessions, and discussions. |
| References |
1. T.H. Cormen, C.E. Leiserson, R.L. Rivest, C. Stein, Introduction to Algorithms, 4th edition, The MIT Press; (April 5, 2022). 2. S.M. LaValle, Planning Algorithms, Cambridge University Press; Illustrated edition (May 29, 2006). 3. A. Bhargava, Grokking Algorithms: An illustrated guide for programmers and other curious people, Manning Publications; 1. basım (31 Aralık 2015). |
Theory Topics
| Week | Weekly Contents |
|---|---|
| 1 | Fundamentals of algorithms, their role in everyday life, history, types of algorithms, definition of execution time, computational complexity. |
| 2 | Algorithm complexity analysis, time and space complexity, best, average, and worst-case analyses with examples. |
| 3 | Basic algorithms and decision-making mechanisms (randomized algorithms, sorting and searching algorithms, best-first search, A*, minimax algorithm). |
| 4 | Fundamental graph and shortest path algorithms, minimum spanning tree algorithms (traversal algorithms, Dijkstra, Bellman-Ford, Floyd-Warshall). |
| 5 | Heuristic and metaheuristic algorithms (local search, simulated annealing, tabu search). |
| 6 | Evolutionary algorithms (genetic algorithm, particle swarm optimization, ant colony optimization, artificial bee algorithm). |
| 7 | Dynamic programming, greedy algorithms. |
| 8 | Midterm exam. |
| 9 | Planning algorithms: basic decision theory, game theory, reinforcement learning, decision-making under uncertainty. |
| 10 | Algorithm ethics, fairness, and open problems. |
| 11 | Student projects. |
Practice Topics
| Week | Weekly Contents |
|---|
Contribution to Overall Grade
| Number | Contribution | |
|---|---|---|
| Contribution of in-term studies to overall grade | 1 | 50 |
| Contribution of final exam to overall grade | 1 | 50 |
| Toplam | 2 | 100 |
In-Term Studies
| Number | Contribution | |
|---|---|---|
| Assignments | 0 | 0 |
| Presentation | 0 | 0 |
| Midterm Examinations (including preparation) | 1 | 50 |
| Project | 0 | 0 |
| Laboratory | 0 | 0 |
| Other Applications | 0 | 0 |
| Quiz | 0 | 0 |
| Term Paper/ Project | 0 | 0 |
| Portfolio Study | 0 | 0 |
| Reports | 0 | 0 |
| Learning Diary | 0 | 0 |
| Thesis/ Project | 0 | 0 |
| Seminar | 0 | 0 |
| Other | 0 | 0 |
| Make-up | 0 | 0 |
| Toplam | 1 | 50 |
| No | Program Learning Outcomes | Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| 1 | X | |||||
| 2 | X | |||||
| 3 | X | |||||
| 4 | X | |||||
| 5 | X | |||||
| 6 | X | |||||
| 7 | X | |||||
| 8 | X | |||||
| 9 | X | |||||
| 10 | X | |||||
| 11 | X | |||||
| Activities | Number | Period | Total Workload |
|---|---|---|---|
| Class Hours | 11 | 4 | 44 |
| Working Hours out of Class | 11 | 5 | 55 |
| Assignments | 0 | 0 | 0 |
| Presentation | 0 | 0 | 0 |
| Midterm Examinations (including preparation) | 1 | 45 | 45 |
| Project | 0 | 0 | 0 |
| Laboratory | 0 | 0 | 0 |
| Other Applications | 0 | 0 | 0 |
| Final Examinations (including preparation) | 1 | 45 | 45 |
| Quiz | 0 | 0 | 0 |
| Term Paper/ Project | 0 | 0 | 0 |
| Portfolio Study | 0 | 0 | 0 |
| Reports | 0 | 0 | 0 |
| Learning Diary | 0 | 0 | 0 |
| Thesis/ Project | 0 | 0 | 0 |
| Seminar | 0 | 0 | 0 |
| Other | 0 | 0 | 0 |
| Make-up | 0 | 0 | 0 |
| Yıl Sonu | 0 | 0 | 0 |
| Hazırlık Yıl Sonu | 0 | 0 | 0 |
| Hazırlık Bütünleme | 0 | 0 | 0 |
| Total Workload | 189 | ||
| Total Workload / 25 | 7.56 | ||
| Credits ECTS | 8 | ||